A Practice-Based Approach to Group Identification in Nonverbal Learning Disorders

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A Practice-Based Approach to GroupIdentification in Nonverbal LearningDisordersGail M. Grodzinsky a , Peter W. Forbes b & Jane Holmes Bernstein aa Department of Psychiatry , Children's Hospital Boston ,Massachusetts, USAb Clinical Research Program , Children's Hospital Boston ,Massachusetts, USAPublished online: 28 Jun 2010.

To cite this article: Gail M. Grodzinsky , Peter W. Forbes & Jane Holmes Bernstein (2010) A Practice-Based Approach to Group Identification in Nonverbal Learning Disorders, Child Neuropsychology: AJournal on Normal and Abnormal Development in Childhood and Adolescence, 16:5, 433-460, DOI:10.1080/09297041003631444

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Child Neuropsychology, 16: 433–460, 2010http://www.psypress.com/childneuropsychISSN: 0929-7049 print / 1744-4136 onlineDOI: 10.1080/09297041003631444

© 2010 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business

A PRACTICE-BASED APPROACH TO GROUP IDENTIFICATION IN NONVERBAL LEARNING DISORDERS

Gail M. Grodzinsky,1 Peter W. Forbes,2 and Jane Holmes Bernstein1

1Department of Psychiatry, Children’s Hospital Boston, Massachusetts, USA, and2Clinical Research Program, Children’s Hospital Boston, Massachusetts, USA

Evidence-based practice, the rigorous conduct of clinical analysis and intervention,includes practice-based evidence. Here, practice data were the source of three “clinicalportraits” used for exploratory analysis of an array of cognitive and social problems in 30children whose neurobehavioral profiles fulfilled psychometric criteria for a nonverballearning disability (NLD). Qualitative analysis of the children’s academic and adjustmentdifficulties revealed patterns of dissociable deficits. These findings argue for at least threegroups within the NLD construct. Identifying such subgroups is of practical importance:More precise characterization of neuropsychological competencies leads to improved inter-ventions and better outcomes.

Keywords: Nonverbal Learning Disorders; NLD subgroups; Evidence-based practice;Learning disabilities; Neuropsychological assessment.

INTRODUCTION

In this paper, we use practice-based evidence (Barkham & Mellor-Clark, 2003) toexplore the neurobehavioral characteristics of children referred to an independent practiceto rule in/out “nonverbal learning disorders” (NLD). Practice-based evidence is a subset ofevidence-based practice (EBP). The latter requires the integration of the best availableresearch evidence, clinical expertise, client values, and available resources (Institute ofMedicine, 2001; Spring, 2007). Application of the principles of EBP will require a broad-ranging reevaluation of both research and practice endeavors (McCall, 2009; Pennington,2009; Weisz, Jensen-Doss, & Hawley, 2006) with effective translation requiring realign-ment of the methodological value systems governing the search for universals on the onehand and ecologically valid descriptions of the individual on the other. McCall (2009, p. 11)calls for “a science that studies how service professionals actually practice … and how thecharacteristics of service delivery contribute to participant benefits”.

Practice-based evidence can be derived from clinical case materials (Gearing, Mian,Barber, & Ickowicz, 2006), outcome data (Hoagwood, Burns, Kiser, Ringeisen, & Schoenwald,2001), and response-to-intervention (RTI; Fletcher, Francis, Morris, & Lyon, 2005). Clinicalobservations in the field are an important element in the larger research endeavor. McKenna

Address correspondence to Gail M. Grodzinsky, PhD, 76 Bedford Street, Suite 21, Lexington, MA,02420, USA. E-mail: [email protected]

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434 G. M. GRODZINSKY ET AL.

and Warrington (1996) argued that it is the critical first step in a three-step process—clin-ical observations → differential response to assessment tools → targeted experimentalparadigms—whereby new insights are developed that, replicated and extended, update theknowledge base with respect initially to the dissociation and nature of syndromes andsubsequently to the development of improved treatment strategies. Clinical observationsare also, however, integrated and interpreted according to rigorous principles by trainedpractitioners. In the larger endeavor, our concern is that we are failing to mine the substan-tial datasets collected by clinicians and are thus missing important information that needsto be incorporated into the greater knowledge base. We need clearly articulated theoriesand methodological tools for deriving the full benefit of this evidence.

We examined issues associated with NLD for several reasons—practice based, clin-ical, and theoretical. From a practice perspective, there has been a marked increase in thenumber of children referred to (neuro)psychologists with problems focused on poor adap-tation in the presence of atypical behavioral patterns. The referral question in these casesis most frequently “rule out—or in—NLD” as “diagnosed” by lay persons, either teachersor parents.

From a clinical perspective, these children do not present as a coherent group nor cantheir behavioral presentations be adequately captured or differentiated by the range of psy-chological tests routinely used in a neuropsychological evaluation. Moreover, their needscannot be addressed with a “one-size-fits-all” intervention strategy or remedial approach.This raises questions of appropriate strategies for both assessment and intervention.

Theoretically, the nature of the NLD entity—and thus how NLD shapes the clini-cian’s work—remains unclear. The original formulation by Myklebust (see below) wasone of potentially dissociable symptoms that could guide intervention and remediation.However, Rourke—since 1987, the primary framer of the NLD discussion—has limitedany consideration of dissociation or subtyping to learning disabilities, with a specificfocus on differentiating verbal learning disabilities (characterized as Basic PhonologicalProcessing Disabilities [BPPD]) and nonverbal learning disabilities (NLD; Rourke, 1995).Characterizing NLD as a syndrome, he has consistently resisted claims for subtypingwithin it.

Rourke’s initial formulation of the NLD construct has been challenged by the neu-ropsychological community: as a unitary construct (Pennington, 2009; Ris & Nortz, 2008;Voeller, 1986); as a syndrome (Denckla, 1983, 1993); and, thus, as a valid diagnosticclassification (Denckla, 1983, 1993; Pennington, 2009). Indeed, many clinicians and clin-ical researchers have been more struck by differences rather than similarities within theNLD presentation as these involve neurocognitive and executive, as well as psychosocial,symptoms (Forrest, 2004; Ris et al., 2007). We have yet to identify, however, whichacademic and neurocognitive symptoms consistently cluster together. We await theemergence of a coherent typology (Semrud-Clikeman & Hynd, 1990).

In the wider practice context, and especially that of the education system, the laydiagnosis of NLD has gained acceptance in spite of the serious challenges to its validity.We believe there are several reasons for this. First, in clinical practice and in educationalsettings, the group of children presenting with problems in learning that cannot be relatedto disturbed language functioning is large. Second, until recently, these children neededdiagnoses to receive specialized instruction and behavioral interventions in the educa-tional setting. Finally, the number of children in the academic mainstream who presentwith the behavioral difficulties subsumed under NLD is increasing with resulting strain oneducation resources.

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SUBGROUPS OF NONVERBAL LEARNING DISORDERS 435

The need for the educational establishment to respond to the needs of such studentshas had unfortunate consequences. The increasing population of children requiringdiagnoses has led to hasty interpretations of the construct, with different professionalshighlighting different aspects of the original criteria without careful review of the original data.Of particular concern is the diagnostic popularity of the [differential IQ profile + differentialacademic achievement profile + social issues] presentation that has, in many quarters, beentreated as the hallmark of NLD.

This focus on IQ profile is particularly problematic. Rourke’s own work has demon-strated differences in the incidence of the (Verbal IQ > Peformance IQ of 10 points ormore)1 criterion in children considered to have NLD: less than 30% of 9- to 15-year-olds(Pelletier, Ahmad, & Rourke, 2001), but 70% of 7- to 8-year-olds (Drummond, Ahmad, &Rourke, 2005). Do these differences undermine the validity of the diagnostic criterion, ordo they reflect an expectable function of the interaction between age at time of testing andthe nature of the measurement instrument? Children who fail to benefit from conventionaleducational practices are likely to lose points on the verbal tasks of standard IQ batteriesthat, beyond 7 or 8 years, typically tap knowledge acquired by listening, reading, and writ-ing in the classroom. For any child with a learning problem, a VIQ-PIQ discrepancy islikely to be attenuated over time. But, if the latter, how do the psychological test scores orprofiles contribute to diagnosis?

These questions are not simply theoretical. Like other clinical practitioners, neuro-psychologists must respond to the larger societal requirements manifest in the regulationsof the Individuals with Disabilities Education Act (IDEA, 2004), which expect thatresearch-based interventions for all students with identified learning disabilities be devel-oped. Such interventions are now conceivable for reading disabilities (Fletcher et al., 2005;Torgesen, 2002). However, our understanding of NLD is not nearly as sophisticated.

Learning Disabilities/Learning Disorders

“Learning disabilities” was originally used to characterize the “verbal disorders” oflearning, involving oral language, listening comprehension, basic reading, word comprehen-sion, math calculation, math reasoning, written language (Fletcher et al., 2005; IDEA, 2004).With the application of neuropsychological models of behavioral function to the assessment ofchildren who fail to make expected developmental progress in learning, the term “learning dis-orders” (Holmes-Bernstein & Waber, 1990; Pennington, 1991) has been increasingly used tocapture the broader range of learning deficits: Anything and everything that the brain learnscan potentially generate a learning problem. From this perspective, “learning disabilities” isa subset of learning disorders.

Nonverbal Learning Disorders/Disabilities (NLD)

Myklebust (Johnson & Myklebust, 1967; Myklebust, 1975) was the first to recog-nize a group of students who experience learning and school adjustment problems, butwho do not fit the criteria for language-based (verbal) learning disabilities. He characterizedthis new group as having “nonverbal disorders of learning” and described seven distinct

1We note that the 10-point discrepancy criterion does not reflect statistical significance (standard deviation) inthe population-based normative data for the tests in question. However, this was the criterion established byRourke from his initial research; it has been the “industry standard” for research studies subsequently.

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nonverbal learning domains that could lead to selective derailment. These include percep-tion or the ability to encode (interpretative); processing of gestures (giving meaning tovisual movement); motor learning (fine and gross motor sequences); body image (visual-ization of one’s own body); and spatial orientation (spatial relationship of the body toother objects). He hypothesized two additional nonverbal domains that could manifestdisordered acquisition: social perception (the ability to size up, to adapt, and to anticipatethe consequences of one’s own behavior in response to socially relevant information) andthe regulation of attention/monitoring systems (the ability to “scan, select, and hold inter-nal events in a manner consistent with his circumstances” (Johnson & Myklebust, 1967,p. 300). This characterization of nonverbal disorders of learning was in line with modernneuropsychological analyses of behavioral breakdown or derailment but did not signifi-cantly impact the work of professional educators and psychologists at the time.

In contrast to Myklebust’s characterization of “nonverbal disorders of learning”with potentially dissociable symptoms, Rourke interpreted nonverbal learning disorders asa unitary entity, framing it as a “syndrome” of nonverbal learning disabilities (Rourke,1987). He subsequently specified a pattern of relative assets and deficits with the “weak-nesses” in visual-spatial-organizational processing, bilateral tactile-perceptual abilities,psychomotor, and novel problem solving. He further hypothesized that the aboveweaknesses resulted in academic (primarily mechanical arithmetic) and social/emotionalmaladaptive behaviors. He then founded his neuropsychological explanation in a model ofdifferential hemispheric organization (Goldberg & Costa, 1981) linking language-baseddisorders to left hemisphere mechanisms and nonverbal disorders to dysfunction in oraccess to right hemisphere mechanisms. Differential hemispheric organization, intra- andinterhemispheric connectivity, and the resulting variations in grey-matter-to-white-matterratios led Rourke to propose his “white matter (WM) hypothesis” (Rourke, 1995): Thedeficits of the NLD syndrome result from disturbances in the myelination (white matter)of the extensive fiber tracts that constitute right hemisphere anatomy. The WM hypothesiswas then extended to a wide variety of congenital as well as acquired conditions.

The field of learning disabilities/disorders in general has benefited from the rapidadvances in behavioral neuroscience over the last 20 years that have confirmed the valueof applying brain-referenced models to the exploration of behavior in both the intact brainand the brain disrupted or derailed by disease. The application of such models has beenproductive in highlighting the relationship between right hemisphere mechanisms and thebehaviors of individuals who meet NLD criteria. The behaviors associated with righthemisphere mechanisms include, for example, visual-spatial processing, storage andretrieval of visual imagery, aspects of attentional arousal and control, processing of thenonverbal aspects of language (e.g., discourse and prosody), processing of emotionallybased stimuli, and processing of tactile stimuli (Lezak, Howieson, Loring, Hannay, &Fischer, 2004). The association between right hemisphere mechanisms and poor nonver-bal processing that is seen in the social, comportmental, and organizational disorders hasbeen well documented in direct evidence accrued from adult and developmental lesionstudies (Brumback & Staton, 1982; Gross-Tsur, Shalev, Manor, & Amir, 1995; Mattson,Sheer, & Fletcher, 1992; Nichelli & Venneri, 1995; Tranel, Hall, Olson, & Tranel, 1987;Voeller, 1986; Weintraub & Mesulam, 1983). The literature in adults does not, however,suggest that right hemisphere insults cause deficits in all of these domains. Nor is there reasonto assume that children with behaviors that can be linked to right hemisphere inefficiencies willexhibit problems in all these domains. Given the plasticity of the developing brain and thecomplexity of neural interconnections, it is unlikely that children will present with either a

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focal impairment or a constellation of deficits that affects all “right hemisphere functions”(in the absence of a system-wide global insult).

Like many of our colleagues (Pennington, 2009; Ris & Nortz, 2008), we believe thatwe lack a basic agreement about the nature of the problem that we need to investigate. Forexample, are we addressing a unitary entity or not? If not (which seems to us most likely),what are the subdisorders of this large category, how dissociable are they, and whichsymptoms might be considered primary, secondary, etc.? In this study we approached thechallenge of diagnostic categorization from the perspective of the children who present forclinical evaluation. Our goal is to characterize more precisely the range of clinical presen-tations seen in this population to guide the development of effective intervention strategiesfor managing the school-age child.

The Practice Context

In the practice setting the focus of analysis is the individual child with his/her partic-ular presentation. “Painting the portrait” (Matarazzo, 1990) of this child is central to ourclinical approach. The latter is based on the evidence-based-practice mandate (Institute ofMedicine, 2001) that requires knowledge of relevant evidence and the expertise to use iteffectively. We follow the literature in domains relevant to our work, derive principlesfrom the knowledge base, and apply these in a rigorous fashion. We are sensitive to cli-ent values and aim to practice in a culturally competent manner at the macrolevels ofpopulation differences (ethnicity, race, class, gender) and at the microlevels of individ-ual experience (living with disease, developmental disability, behavioral disorder). Thereview-of-neurobehavioral-systems framework guides the collection of behavioral datathat are obtained from historic sources, direct and elicited observations, and scores/score profiles derived from targeted psychological instruments. Relevant data are bothqualitative and quantitative. We employ a “mixed” assessment strategy: a standard clin-ical protocol to provide a common dataset for cross-child comparisons and flexible testselection to explore individual differences via selective hypothesis testing (Bernstein,2000; Bernstein, Kammerer, Prather, & Rey-Casserly, 1998; Holmes-Bernstein &Waber, 1990). This approach has guided our practice in both the medical arena and pri-vate practice setting.

The study was conducted with clinical data collected over 10 years in a private prac-tice setting (Greiffenstein, 2003). The impetus for the investigation was the recognition thatchildren who were referred to rule in/out NLD nonetheless presented with very differenttemperaments, learning styles, and ways of dealing with the world. From a clinical per-spective three groups of children began to emerge; groups that could be differentiated interms of their respective neurocognitive, academic, and social adjustment profiles. Thisled us to question the unitary nature of the NLD construct.

Our first step was to conduct an informal survey of our “community of practice”(Lave & Wenger, 1991; Wesley & Buysee, 2006) asking for their opinion as to the pres-ence of such dissociations within the NLD construct and the potential benefits in uncover-ing different profiles (Spring, 2007). Colleagues were unanimous on both counts:Children in nonmedical settings who fulfill criteria for NLD do not appear to be a unitarygroup; the differences between them are important for management and intervention. Thisthen led to a literature search of the history/conceptualization of NLD and the presence ofdissociable symptoms within the larger construct. The continuing debate about the natureof NLD and possible subgroups confirmed the value of further investigation.

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The clinical descriptions of the three groups of children yielded distinct “portraits”(Matarazzo, 1990). The clinical portraits below provide prototypical examples of thesethree groups. The portraits highlight individual differences among children that are noteasily captured by psychological test scores/profiles but are critically important for theclinician’s understanding of the child and the framing of the individualized managementplan.

CLINICAL PORTRAITS

Portrait #1: “Danny”

Referral and background. Danny, a 10-year-old fourth grader, was referred dueto difficulty summarizing reading passages, recalling math facts, and organizing writtenwork. Mother described him as a cuddly baby, socially responsive with a quick smile.Developmental milestones were acquired within normal limits. Acquisition of academicmilestones was harder to achieve. Decoding was delayed, although he eventually“cracked” the letter-sound code; nonetheless, reading remains slow and dysfluent. Graph-omotor difficulties required occupational therapy in the primary grades; handwriting contin-ues to be poor. A diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD)Inattentive-type was questioned, though parents wondered if he had a learning disability.Mother recalled, “He has difficulty retelling a movie or story; it’s just a shopping list ofdetails, he misses the main point every time.” Socially, he is likable with a good sense ofhumor and has friends at school. He enjoys playground activities, though he is not veryathletic. When social conflicts arise, he is happy to adopt solutions rather than to createthem.

Behavioral observations. During the evaluation, Danny was good natured andinterested in social interaction. However, his participation was limited by slow processing:he “waded through molasses” while struggling to work out what had been said, to evaluatewhat was required, and to formulate an appropriate response. Tasks that other childrenachieve with reasonable effort led to fatigue. He frequently signaled awareness of hisstruggles with “Uh! That’s not what I meant! Whatever!” He could deploy attentionalskills appropriately; although mild inattentiveness was reported in school. Basic languageskills in conversation were intact with appropriate vocabulary and syntactic variety.Speech parameters and behavioral modulation (emotional reactivity, verbal activity, andinterpersonal boundaries) were within normal limits. Motor activity was relatively high onthe fidgety dimension. He was able to initiate, to sustain, to inhibit, to shift, to reason, toplan, and to monitor, albeit at a slowed pace. However, he had difficulty mobilizing theseskills as tasks increased in complexity. Gross motor and fine motor skills were intact.

Danny was relatively passive in his approach to tasks, cooperating willingly, thoughrarely initiating clarification; performance suffered when he was unsure (docile demeanor;drifting gaze), and he had difficulty making decisions (e.g., on a snack or writing about hisfavorite game). He did not intuitively “stretch information,” explore or elaborate ideas,and the effort required to fulfill tasks at a basic level often led to fatigue.

Neuropsychological highlights. The neuropsychological profile revealedwell-developed verbal knowledge and vocabulary with good literal comprehension. How-ever, difficulties with word retrieval and verbal formulation were evident. Provided with

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support to break down task requirements, Danny could proceed but was vulnerable to“overload.” Tasks tapping sequential working memory elicited passive learning strategies.He used an immature outer-perimeter approach to copy the Rey-Osterrieth ComplexFigure (ROCF; Osterrieth, 1944) failing to appreciate the basic rectangular logic andadded lines in a somewhat random fashion. Under recall conditions, he labeled the designand simply recalled his label.

Academics. Academically, Danny labored to produce significant output. He wasa slow but accurate reader. Recognition of low-frequency words was arduous, consistentwith minimal use of context to aid decoding. Spelling was phonetically driven with fewpoints earned on multisyllabic, nonphonetic words. Marked difficulty was seen in generat-ing organizational strategies for written assignments. Sentences were barebones and repet-itive in nature. Math output was similarly prolonged with or without pencil and paper,though age-appropriate concepts were available. There were few spatial errors. Retrievalof math facts was accurate but slow; Danny relied on finger counting rather than calcula-tion, having difficulty when problems required the processing of multiple components.

Management approach. Instructional programming for Danny targeted theimpact of processing speed and organizational issues: general behavioral management—direct instruction in time management, pacing of effort, regular checking-in expectationsto monitor materials for homework/test preparation; in listening contexts—strategies forfocusing attention on important information to minimize being bogged down in irrelevantdetail; for writing assignments—direct instruction in proactive (metacognitive) learningstrategies, e.g., underlining, outlining, note-taking, and summarizing, systematic use ofword-processing software, extended time to implement strategies taught to him.

Portrait #2: “Jay”

Referral and background. Jay, a 12-year-old sixth grader, was referred due toincreased academic difficulty despite longstanding special education services. Parentsrecalled him as a quiet baby, slow to explore on his own, quite willing to sit and watch hissiblings scoot about. As a preschooler, he avoided climbing structures preferring to sit atthe sand box; at school, he actively avoided puzzles and graphomotor activities. He sel-dom initiated social interactions, favoring parallel play. Problems with balance, poorawareness of his body in space (including left-right confusion), and inability to catch aball led to avoidance of playground and gym activities resulting in reduced opportunitiesfor peer interactions. Presently, he has a couple of friends who, like him, are into videoand computer games.

Teachers described Jay as a kind, polite student who tries his best. Consistent withhome reports, they noted mild inattention, difficulty following directions, and handwritingthat is barely legible. His sense of time is poorly developed; he can read a clock but isunable to judge elapsed time.

Behavioral observations. In evaluation, Jay presented as a likable youngster,soft-spoken, and shy. He did not initiate conversation but readily understood andanswered questions. Comprehension, vocabulary use, syntax, and speech parameters wereall within normal limits. Behavioral modulation was not an issue. Goal-oriented problem-solving skills were available: he could initiate, inhibit and shift. He was dogged in his

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compliance with task demands, but he frequently failed to grasp that he was not succeed-ing. Reasoning, planning, and monitoring output were available at a basic level: If herecognized that a response was inadequate, he would then have no idea how to go backand re-engineer it. He could plan an initial response to a written assignment but could notmaintain an internal representation of the guiding theme throughout a narrative. Histhinking/learning style was notably concrete: He did not easily integrate information on anindependent basis. Gross motor functioning was grossly within normal limits. Hestruggled with both fine motor and graphomotor skills. Emotionally, he was aware of thedifference between himself and his peers, anxiously wanting to “belong” but not knowingwhat to do.

Neuropsychological highlights. The most telling aspects of Jay’s performanceinvolved constructional materials and copying complex designs with pencil and paper.When constructing block designs, he was insensitive to internal relationships and brokeconfiguration often. He regularly analyzed abstract geometric forms in terms of individualdetails, failing to grasp the logic of the figure. Of note was his association of the parts ofhis productions to real-world referents (Developmental Test of Visual Motor Integrationitem #17 was produced as six radiating lines that were then verbally encoded as “A cat’swhiskers”). He approached the ROCF as if it were “a foreign country” not knowing howto go about the task, where to start or how to proceed. He eventually produced, deliber-ately but haphazardly, a set of isolated fragments lacking cohesion. His recall suggestedthat he had essentially remembered what his hand had done (Waber, Bernstein, & Merola,1989). The production was consistent with constructional apraxia (Benton, 1967). Mem-ory for visually represented information was completely undermined by the severe limita-tions in initial encoding.

Academics. Academically, Jay exhibited strong word reading skills. He wasrelatively successful in comprehension of detail but less so when inferential reasoning wasrequired. Asked to produce an essay, he did not hesitate to begin but rapidly floated offinto a stream of consciousness, flooding his narrative with details irrespective of theirrelevance. In math, he could follow the algorithm to add and subtract multidigit numberswith regrouping using graph paper. He could not maintain the alignment of columns with-out the latter. He did not have an intuitive sense of place value, nor of concepts ofdistance, amount, or proportion. He could not evaluate the plausibility of his answersresulting in errors that were extreme.

Management approach. Instructional programming for Jay highlighted: gen-eral behavioral management/listening set—basic teaching level, very explicit instruc-tional stance, precise targeting of concepts, intensive use of templates, cues and scaffoldedtexts; in math—emphasis on functional skills, intensive drill/practice, real-life applica-tions, explicit verbal rules, concrete, step-wise algorithms, minimal visualization; in writ-ing—keyboarding, word-processing software, templates, oral rehearsal of topic sentences.

Portrait #3:“Kate”

Referral and background. Kate, a 14-year-old eighth grader, was referred dueto a history of disorganization, low frustration tolerance, and emotional reactivity. Byreport, she was not a smiley baby and misconstrued peek-a-boo games as scary. She was

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an early talker. Motor development was within normal limits. Self-regulation has alwaysbeen problematic. She experiences difficulty with transitions and changes in routines;meltdowns remain frequent and intense. She is baffled when her behavior annoys her sib-lings. She resists parent surveillance with her homework, arguing and slamming doors inresponse to their suggestions.

At an early age, Kate taught herself to read and became a voracious reader, at timespreferring reading in a corner to playing with her classmates. By the latency period, how-ever, she actively sought relationships with other children and expressed sadness whenignored or rejected. She remained a solitary figure on the playground, being the sort ofchild who has only one friend, one friend only, and one friend at a time. Academically, herrecall of information is excellent. Teachers describe a strong need for routine and rules.Her reactions to social and cognitive situations lack spontaneity: She is unable to developan intuitive sense of the dynamic nature of such situations and react to them.

Behavioral observations. During the evaluation, Kate’s demeanor was uncom-promising and humorless. An articulate child, she readily took part in conversation. Shewas, however, defensive about her performance, resorting to anxious laughter, worryingabout what the examiner was writing, and criticizing test materials (e.g., “This makes nosense! Who made this up?”).

Kate deployed attentional skills effectively. Conversation elicited appropriatevocabulary and syntactic variety. Rate and articulation of speech were within normallimits. Behavioral modulation was insecure. Poor inhibitory control of voice volume andverbal activity level was reflected in too loud a voice and too many questions, which wascompounded by anxiety. Emotional reactivity was heightened. Motor activity level waswithin normal limits. In ongoing activities, Kate initiated and sustained activities. She haddifficulty with the inhibition of salient but nonrelevant information and in shifting fluentlybetween tasks. Reasoning and planning were typically linear. Gross motor skills were ade-quate, although gait was awkward. Fine motor and graphomotor skills were available.

Neuropsychological highlights. The neuropsychological profile revealedsophisticated vocabulary, fluent comprehension, and effective verbal formulation withassociated strengths in reading and spelling. Reduced flexibility in thinking was salient:Examiner inquiry was needed to elicit additional information on open-ended questions.Relative weakness was noted on visual integration tasks, especially with a motor compo-nent. Kate initially refused to copy the ROCF without a ruler but eventually proceeded ona careful line-by-line basis. Immediate recall revealed a similar part-oriented style withgood preservation of details. On construction tasks, her approach was trial-and-error,rarely gaining momentum with practice nor earning additional points with extra time. Onnonmotor spatial-reasoning tasks she fared better, particularly with multiple-choiceformats that made fewer demands on visual working memory.

Academics. Academically, reading skills were well above grade level, thoughreading aloud lacked expression. Literal comprehension was excellent; inferential ques-tions were problematic. Computational math skills were appropriate for age; of note was arefusal to attempt items in unfamiliar formats (“I haven’t learned that yet!”). Kate usedcircuitous linear methods on multidigit problems that were accurate but cumbersome andtime-consuming. In fact, her linear approach was not an issue until she transitioned froman algorithm-based math program to a concept-based curriculum where the skill-demand

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match problem surfaced. Written language demands elicited immediate and immobilizinganxiety for Kate, prompting a barrage of questions, unable to start the task. She wasannoyed at the examiner for making her write and preferred to dictate. After considerableguidance from the examiner, she launched her first sentence. Her story was short, simple,and concrete—in striking contrast to her well-developed oral language.

Management approach. Instructional programming targeted general behav-ioral management—social skills/cognitive control training (recognizing critical situations,developing effective self-talk strategies, rumination reduction); in listening contexts—explicit instruction models, guided cuing strategies; in writing tasks—staged outlining,focus on ownership of written products; in reading—inferential comprehension enhance-ment (“wh” questions, reciprocal teaching, story maps, and predicting outcomes).

The notable differences in behavior portrayed above—though striking, very familiarto experienced clinicians and critical for diagnostic formulation—are not easily capturedby the score structure of psychological tests. They were obtained via systematic scrutinyof qualitative aspects of the child’s performance in both evaluation and real-world set-tings. An appreciation of such variables is critical in understanding the children portrayed.Danny is socially engaged and motivated, but processing of information is poorly syn-chronized with the result that executive skills are undermined across the board and avail-able resources are rapidly overwhelmed as tasks increase in complexity. Jay is a quiet,likeable youngster, willing and motivated. He approaches learning through the lens ofunintegrated elements; apparently lacking the ability to generate internal representationsor guiding schemata, he works from the bottom up, relying on concrete materials, real-world referents and personal experience. His lack of fluid reasoning skills adverselyaffects his neuropsychological development across domains and limits his everyday func-tioning. Kate is anxious and inflexible; lacking in appreciation of the “big picture,” she islimited to literal understanding and linear problem solving. She does not lack socialawareness and empathy but is unable to use this knowledge in ongoing social encounters.

Group Labels

The portraits of Danny, Jay, and Kate serve to exemplify three groups of childrenwho were initially referred to rule in/out NLD. In the learning disability literature much hasbeen made of the potential for “subtypes” or typologies (Forrest, 2004; Hendriksen et al.,2007; Ris & Nortz, 2008). We wish to avoid misunderstanding of our position at the outset.Our approach to classification at this point is conservative. We describe the clinical presen-tations simply as “subgroups.” No claims are being made for a typology nor are we boundto any a priori commitment to NLD as a syndrome, or to the characterizations that we pro-vide as “subgroups.” We believe that such positions are premature and need to be bettersubstantiated by research. To date, this has not occurred. We have therefore been careful tocharacterize our grouping as such and refer throughout to “subgroups” of children.

For the purpose of this study the subgroups were labeled according to the clinician’sjudgment of their most salient psychological deficit as this emerged from the neurobehav-ioral profile obtained from the evaluation process as a whole. The labels reflect the neu-ropsychological perspective of the practitioners. Children like Danny are seen as havingproblems synchronizing the necessary elements (visual and verbal) to process (input) and

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produce (output) at an appropriate cognitive rate or tempo. They cannot efficiently scan asubset of information, select relevant information and then organize that information(DeLuca, 2008) to manage the “output” required in a standard academic curriculum. Wehave characterized this profile as the Processing Efficiency group (PROC-EFF). Youngsterslike Jay struggle due to underlying deficits in the integration of concepts; they are unableto weave together elements to create a “whole” mental representation. Children with thisprofile we assigned to the Concept Integration group (CONC-INT). Children like Katewere characterized in terms of the most salient impact of their linear style in the everydayworld. This style limits online processing of the multiple, rapidly changing cues that arenecessary for effective social functioning. Its most dramatic manifestation is thus in socialadaptation. It also has a significant impact in the school context, most notably when thecurriculum requires exploratory and flexible approaches to problem solving. This is theSocial Adaptation group (SOC-ADAP).

METHODS

Participants

For this study, a systematic chart review of 435 consecutively seen children referredfor neuropsychological assessment over a span of 10 years was undertaken. Children wereseen in a private practice serving a largely middle- to upper middle-income population. Asubset of this larger sample was diagnosed with NLD on the basis of distinctive behavioralprofiles. These profiles are not always accompanied by the psychometric criteria for thedisorder. For this initial study we chose to focus on children who did meet psychometriccriteria (Rourke, 1995). Thirty children were identified. The study sample was comprisedof 18 boys and 12 girls ranging in age from 7.1 years to 16.2 years. None of the childrenhad any medical condition that could be presumed to affect behavioral function directly(head injury, neurofibromatosis, cardiac conditions, cancer, and epilepsy). We alsoexcluded children with a diagnosis of autism or major psychiatric condition (bipolar disor-der, psychosis). We did not exclude children with appropriately managed attention andmood problems since this exclusion would not be consistent with clinical practice. Fivechildren had been previously diagnosed with ADHD and were stable on medication; ninechildren were stable on medication for anxiety and/or mood management. Neither ADHDnor anxiety disorder was overrepresented in any one of the three groups.

Strategy for Group Membership

Children were assigned first to the NLD category and then to the subgroups on thebasis of the diagnostic formulation provided by the clinical assessment. The neuropsycho-logical clinician works with three diagnostic nosologies: (a) the Diagnostic and StatisticalManual, fourth edition (DSM-IV; American Psychiatric Association, 1994) is primarilyfor reimbursement; (b) models of brain-behavior relationships are the basis for neuropsy-chologically framed diagnostic formulations; (c) the education system uses categories thatreflect problems in specific academic skill areas. The DSM-IV also categorizes some, butby no means all, of the diagnostic categories of neuropsychology and education. In ourpractice, we routinely offer more than one level of diagnosis: (a) a formal statement of theneuropsychological forces at play that reflects the neuropsychological knowledge base(e.g., “the neuropsychological protocol as a whole is consistent with a primary attention

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disorder”) and (b) a statement as to where this fits in the nosology required for reimbursement(e.g., “this does—or does not—fulfill criteria for ADHD”). For a child presenting with theNLD picture, the diagnostic categorization can include neuropsychological formulations framedin terms of the primary psychological processes identified as well as DSM-based categories(e.g., motor dyscoordination disorder, math disorder, and/or generalized anxiety disorder).

For this study, the diagnostic formulations assigned by the clinician at the time ofassessment served the function of the clinical sort. The diagnostic formulations are theproduct of the clinician’s expertise in, first, accessing the relevant knowledge base andthen integrating this with the particulars of the individual life circumstances of the givenchild and family. Data come from: clinical interview (presenting problem, developmentalhistory, family variables); review of neurobehavioral systems (direct and indirectbehavioral observations, standardized questionnaires/rating scales, psychological testperformance); and intervention outcomes (the match of the individual child with develop-mental expectations and those of the educational setting and curriculum). Children fulfill-ing NLD criteria were 7% of the larger cohort. Of these children, 27% were in the PROC-EFF group, 47% in the CONC-INT group, and 27% in the SOC-ADAP group.

Measures

As noted above, the larger clinical strategy is a review of neurobehavioral systems thatis consistent across children. Selection of measures is based on a combination of fixed andflexible (hypothesis testing) models. Not all tests were administered to all children and theclinician updated test instruments over the course of the data collection as new versionsbecame available. Test score by test score comparisons across children are thus not possible.Missing data (parental noncompliance, e.g., behavioral rating scales, or child noncompli-ance) posed methodological limitations. In cases of missing variables, due to a reduced sam-ple size, averages were derived rather than deleting an entire variable from the analysis.

The measures subjected to analysis (see below) were selected from the comprehen-sive neuropsychological evaluation of each child employing up-to-date measures in therelevant domains at the time of assessment. Identification of NLD was based on performancein the three domains that are thought to describe the key features of NLD, namely, a dis-crepancy between verbal knowledge and nonverbal reasoning, better basic reading(decoding) than basic math skills (computation), and impaired motor dexterity. Thus, to beincluded in the study a child had to meet the following “probable” criteria for NLD(Drummond et al., 2005; Pelletier et al., 2001; Rourke, 1995): (a) a Verbal Quotient (VIQ)Verbal Comprehension Index (VCI) or a Performance Quotient (PIQ)/Perceptual Reason-ing Index (PRI) of 70 or above on the WISC-III/WISC-IV, respectively (see below); (b) averbal knowledge (VIQ/VCI) > nonverbal reasoning (PIQ/PRI) discrepancy of at least10 points WISC-III/WISC-IV, respectively; (c) a reading decoding > math calculation dis-crepancy of at least 10 points, and (d) mild to moderate impairment on a measure of motordexterity. The measures used were as follows:

Wechsler Intelligence Scale for Children, Third Edition (WISC-III; Wechsler, 1991)and Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV; Wechsler, 2003)2

2The corrected correlation coefficients of composites for the WISC-III and WISC-IV are strong rangingfrom .89 for FSIQ–FSIQ; WISC-IV VCI and WISC-III VCI is r = .88; WISC-IV VCI and WISC-III VIQ is r =.87. PRI-PIQ and WMI-FDI is r = .72. WISC-IV PRI and WISC-III POI is r = .72 with the correlation betweenWISC-IV PRI and WISC-III PIQ r = .74 (WISC-IV Manual, pp. 62–63).

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were used as indicators of intellectual functioning and to establish verbal > nonverbaldiscrepancy, as described above. WISC-III data were collected for 25/30 children; WISC-IV data were collected for 5/30 children.

Reading decoding was indexed by either the Test of Word Reading Efficiency(TOWRE), Sight Word Efficiency subtest (Torgesen, Wagner, & Rashotte, 1999) or theWoodcock Reading Mastery Tests-Revised (WRMT-R), Word Identification subtest(Woodcock, 1998). Correlation figures for the TOWRE Sight Word Efficiency and WRMT-Rin a sample of randomly selected children are between .86 and .89 among children who wereconsidered at risk for reading failure .92–.94 (see TOWRE Manual, p. 73).

Math computation was indexed by one of the following: Kaufman – Test of Educa-tional Achievement (K-TEA) Math Computation (Kaufman & Kaufman, 1997) or Wech-sler Individual Achievement Test (WIAT/WIAT-II) Numerical Operations (Wechsler,1992, 2002). Correlation coefficients between Math Computation and Numerical Opera-tions are .79 for grades 1–5 and .86 for grades 6–11 (K-TEA, Manual, pp. 104–105).

Motor dexterity was indexed by the Grooved Pegboard (Klove, 1963). This is acommonly used measure that assesses speed and accuracy of eye-hand coordination. Allsubjects obtained a standard score of < 80 with either the nonwriting hand or both handscombined (Rourke, 1995).

Additional Dependent Measures for Group Comparisons

In addition to the measures identifying NLD, we examined the children’s perfor-mance on the following measures.

Developmental Test of Visual Motor Integration, 5th edition (VMI; Beery

& Beery, 2004). The VMI is a paper-and-pencil, untimed measure tapping visuo-motor integration, spatial organization, and visuoperceptual ability (Baron, 2004). Proto-cols administered prior to 2004 were rescored to be consistent with the fifth edition. It washypothesized that all groups would perform below age expectations but that the CONC-INT subgroup would perform significantly worse than the other two subgroups.

Rey-Osterrieth Complex Figure (ROCF; Bernstein & Waber, 1996;

Osterrieth, 1944). The Developmental Scoring System of the ROCF (DSS-ROCF) yields an organizational score, a style rating and an accuracy score for structuraland incidental elements. In addition, the data were subjected to two clinical sort procedures(Waber & Holmes, 1985, 1986). These were conducted by the second author who wasblind to group assignment. The first sort was guided by a “goodness-of-organization”criterion with each condition (Copy [C], Immediate Recall [IR], Delayed Recall [DR]) ofthe overall ROCF protocols being sorted independently. The second sort was of eachROCF protocol (C+IR+DR) as a whole. It was hypothesized that all groups would per-form below age expectations but that the CONC-INT group would perform significantlyworse than the other two groups.

Verbal Fluency. Two conditions of timed verbal naming were analyzed. For 8years and up, the Phonemic Naming (F-A-S) condition was from the Delis-Kaplan Execu-tive Function System (D-KEFS) Verbal Fluency subtest and administered and scoredaccording to the manual (Delis, Kaplan, & Kramer, 2001); for younger children the scor-ing was based on number of correct items generated in 1 minute (Halperin, Healey,

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Zeitchik, Ludman, & Weinstein, 1989). For the Category Naming condition (animals), theproductions of all children were scored according to the Halperin et al. criteria. Verbal flu-ency has been related to cognitive or mental speed of processing with poorer performanceon fluency tasks associated with slow processing speed (Strauss, Sherman, & Spreen,2006, p. 518). It was hypothesized the PROC-EFF group would perform more poorly thanthe other two groups.

Social functioning was assessed by the Internalizing and Externalizing Compositescores on standard measures, either the Child Behavior Checklist–Parent or Teacher Form(CBCL; Achenbach & Edelbrock, 1983) or the Behavior Assessment System for Children,Parent and Teacher form (BASC, BASC-2; Reynolds & Kamphaus, 1983, 2004).3

Social adjustment data were available for all but one of the children in the sample(29/30, 97%). Parent rating scales (BASC or CBCL) were available for all 29/29 children;while only 25/29 (86%) teacher rating scales were available. Seventy-nine percentobtained a T-score of 60 or above on the Internalizing scale on either the parent (14/29;48%) or teacher (8/25: 33%) behavioral rating scale. On the Externalizing scale, 9/29(31%) obtained a T-score of 60 or above on the parent form; 5/24 (21%) obtained aT-score >60 on the teacher form.

RESULTS

Table 1 gives the demographic and inclusion data for all three groups. Comparisonswere made with Analysis of Variance (ANOVA). For significant group effects, post hocanalyses for all pair-wise contrasts throughout were conducted with Least Squares Differ-ences controlling alpha at p < .05. There was no main effect of age or socioeconomic sta-tus (SES) on group membership (p > .10 throughout). There was also no significantinteraction of group with age or SES on any of the inclusion variables (p > .10).

There were main effects of subgroup on FSIQ, VCI, and POI/PRI, GroovedPegboard, and academic scores (decoding and math scores). Even though all groups ful-filled minimal inclusion criteria, post hoc analysis using Least Squares Means (3 × 3ANOVA) showed that the CONC-INT group performed significantly lower than both thePROC-EFF and SOC-ADAP subgroups on all inclusion variables, FSIQ (p < .001), VCI(p < .0003), POI/PRI (p < .0002), Grooved Pegboard (p < .03), Reading (p < .0001) andMath (p < .0002). There were no differences, however, between the PROC-EFF and SOC-ADAP group. There were no main effects of group on VIQ > PIQ or VCI > POI/PRI

3Correlations between the BASC and BASC-II on the Teacher Rating Scales-Child (TRS-C) were strong(.94 – .98 on Composites) with similar high correlations on the TRS-A (.93–0.92 on Composites) (BASC-IIManual). The BASC was also highly correlated with the CBCL on composites. Correlations on the BASC-TRSand PRS Behavioral Symptom Index (BSI) and the CBCL-TRF and PRS for Total Problems was .81 and .73,respectively. These composite scores correlate highly and are believed to tap the same constructs (BASC Man-ual, pp. 148 and 179, respectively). More specifically, Internalizing Problems correlations on the teacher-ratedchild forms ranged from a low of .71 to a high of .87 (BASC-II). On the adolescent form, the correlations rangefrom a low of .66 to a high of .93. This pattern is similar to that reported between the TRS-BASC and teacher’sreport form (Achenbach, 1991). In other words, externalizing problems rated more consistently across instru-ments than internalizing problems. Given the strong correlations, scores from either measure were considered asacceptable alternates. Similarly, comparable correlations are reported on the parent forms (BASC-II and CBCL:ages 6–18) of these measures (Internalizing .65–.75, Externalizing .74–.83, Total Problems .73–.84), which werevery similar between the original PRS (BASC) and CBCL. Given the robust correlations, the Internalizing andExternalizing scales between the PRS and CBCL were considered compatible when describing social-adaptivefunctioning among all subjects.

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discrepancy. There was no difference on the FSIQ, VCI, POI/PRI, or Processing SpeedIndex (PSI) between subjects given the WISC-III and those given the WISC-IV. The agesof the WISC-III and WISC-IV subjects were also not different. Additional analyses wereperformed on individual subtests of the WISC. There were group by gender interactionsfor two WISC variables. For Similarities, group by gender interaction showed that withinthe CONC-INT group, girls scored lower than boys relative to other subgroups, whereasgirls score higher than boys in the PROC-EFF and SOC-ADAP groups (p = .0046). ForPOI/PRI, group by gender interaction analyses showed a marginal effect (p = .057), suchthat within the SOC-ADAP group, girls scored higher than boys, whereas boys and girlswere comparable in the other groups.

Table 2 presents the data for the investigational tasks. There was no main effect ofsubgroup for the Processing Speed Index (PSI). However, there was an age effect for PSIsuch that with increased age, performance on timed measures (PSI, Coding, and SymbolSearch) decreased. This effect of age was driven primarily by the reduction in Codingperformance with increasing age within the SOC-ADAP group (subgroup × age interac-tion p < .03). The mean ROCF Organization scores for all groups were low across theboard. No main effects of subgroup were seen on the Copy, Immediate Recall, andDelayed Recall conditions, or on the clinical sort procedures. The ROCF was sensitive toorganizational difficulties, but not specific to group (Grodzinsky & Barkley, 1999). Maineffects of subgroup were found on the VMI, Reading Decoding, and Math Computation

Table 1 Mean (SD) of Demographic Characteristics and Inclusion Criteria by Subgroup1.

VariableProcessing

Efficiency n = 8Concept

Integration n = 14Social

Adaptation n = 8

Age in months 132.8 (25.6) 139.4 (32.2) 144.7 (34.0)Gender (% boys) 75 50 62.5SES2 1.21 (0.26) 1.5 (0.74) 1.21 (0.39)FSIQ3 100.0 (12.3) 84.4 (5.9) 107.4 (13.8)VCI3 115.1 (10.8) 101.1(8.2) 120.4 (11.5)POI/PRI3 84.6 (9.2) 74.2 (8.8) 95.5 (12.2)Grooved Pegboard4

Dominant 75.7 (17.1) 67.0 (17.2) 89.1 (17.0)Nondominant 71.0 (17.0) 55.4 (12.0) 81.6 (14.3)

Reading5 (decoding) – Math6 (computation) Discrepancy

19.0 (10.1) 15.71(11.8) 16.6 (8.36)

VIQ-PIQ/VCI-PRI Discrepancy 27.0 (8.4) 24.1 (10.0) 23.4 (5.6)

1Entries are subgroup means (standard scores) with SD given in parenthesis.2Hollingshead Computed Code using the Barratt Simplified Measure of Social Status (BSMSS): Total

score represents Total Education plus Total Occupation reflecting a range of scores between 8 and 66. It is anordinal score reflecting a weighted score of educational attainment and occupational prestige (3:5): 1 reflect-ing the highest level of attainment and the number 5 representing an assumed lower class SES (Hollingshead,1975).

3FSIQ, VCI, POI/PRI: WISC-III/WISC-IV.4Standard Score.5Decoding: Woodcock Reading-Mastery (Word Identification) or Test of Word Reading Efficiency (subtest:

Sight Word Efficiency).6Kaufman Test of Educational Achievement (Computation) or Wechsler Individual Achievement Test/

WIAT-II (Numerical Operations).

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with post hoc analysis again revealing the reduced performance of the CONC-INT groupas compared with both the PROC-EFF and SOC-ADAP groups.

Table 3 shows scores on social adaptive functioning measures for each group. Therewere no significant differences among the three subgroups for the Externalizing orInternalizing scales on either both parent and teacher forms.

Tables 4 through 6 present the hypothesized subgroup characteristics. Analyseswere conducted using unpaired t-tests. Results reported below use the Satterthwaite methodin order to drop the assumption of equal group variances. To test the hypothesis that thePROC-EFF group has slower processing speed, the PSI was chosen for graphomotor speed,

Table 2 Means (SD) for Investigational Tasks by Subgroup.

MeasureProcessing



Adaption n = 8ANOVA p < .05

PSI (WISC-III/IV) 90.87 (17.06) 81.42 (12.24) 91.12 (21.1) nsDSS - ROCF

Organization (based on age)COPY 3.62 (2.19) 2.53 (1.8) 4.28 (2.49) nsIR 4.12 (3.87) 3.53 (2.18) 4.42 (4.27) nsDR 4.25 (4.39) 3.58 (3.39) 4.57 (4.19) ns

Accuracy: Structural Elements (raw scores)COPY 23.71 (1.49) 22.38 (3.8) 22.0 (4.0) nsIR 17.28 (5.3) 17.23 (7.6) 16.85 (6.3) nsDR 17.57 (5.2) 17.0 (6.8) 17.0 (6.19) ns

Accuracy: Incidental Elements (raw scores)COPY 37.14 (1.57) 34.07 (4.71) 35.85 (2.98) nsIR 23.75 (9.11) 25.53 (7.3) 23.28 (6.26) nsDR 24.25 (9.69) 23.58 (8.86) 24.85 (7.49) ns

Verbal Fluency1

Letter 10.28 (2.6) 8.28 (3.51) 9.8 (3.2) nsCategory 12.0 (3.31) 8.0 (3.28) 10.12 (3.31) .04

VMI 83.0 (6.1) 69.5 (8.9) 80.0 (11.14) .005Reading Decoding 115.12 (5.89) 95.42 (9.24) 112.25 (7.06) < .0001Math Computation 96.12 (11.39) 79.71 (7.04) 95. 62 (12.2) .0002

Note. Rey Osterrieth Complex Figure Organization Score is an ordinal score that ranges from 1–13.1Scaled Score (SD).

Table 3 Means (SD) for Behavioral Assessment Data/Social Adaptive Functioning.

MeasureProcessing



Adaptation n = 8ANOVA p < .05

P: Externalizing1 53.62 (7.02) 51.50 (13.53) 60.71 (6.92) nsP: Internalizing 57.5 (8.76) 58.78 (10.31) 63.85 (8.91) nsT: Externalizing2 47.2 (7.3) 49.7 (11.6) 55.1 (8.2) nsT: Internalizing 57.2 (4.9) 56.8 (9.2) 54.0 (10.8) ns

Note. Behavioral Assessment Data: CBCL or BASC.1P = Parent; T = Teacher.2Teacher Forms: Two missing teacher forms each in Processing Efficiency and Concept Integration groups.

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and Verbal Fluency (Phonemic and Category Naming) were chosen for verbal speed.Table 4 reveals the predicted lower scores on the PSI/Verbal Fluency measures for thePROC-EFF group was not upheld for either the PSI, t(11.9) = −0.85, p < .41, or PhonemicNaming, t(13.2) = −1.15, p < .26. Group differences were found on Category Naming,although not in the proposed direction, t(10.3) = −2.23, p < .04.

To test the expectation that the CONC-INT group has significantly greater deficitsin spatial processing functions, specifically perceptual reasoning and spatial organiza-tion, the POI/PRI index (WISC) and ROCF were chosen, in addition to Math Computa-tion, as a functional representation of a “visual spatial processing” variable. Table 5shows there was a significant difference in the hypothesized direction between theCONC-INT and all other groups on POI/PRI, t(27.4) = 4.18, p < .0003, and Math Com-putation, t(26.4) = 5.02, p < .0001, but no difference was found on the DSS-ROCF,t(25.8) = 1.80, p < .08, or its clinical sort. Since the CONC-INT group was noted earlierto have significantly lower FSIQ scores, a multivariate regression analysis was used tocompare the visual-spatial results for the CONC-INT group with other children adjust-ing for FSIQ differences between the groups. As expected, the CONC-INT group wasmore like the other groups after adjusting for FSIQ differences. In this adjusted analysis,the CONC-INT group differed significantly from the others only on Math Computation(p < .03). The groups were nearly identical after adjustment on the POI/PRI. The perfor-mance of the CONC-INT group on the ROCF and VMI was between one-half and onestandard deviation worse than the others but, with this small sample size, those differ-ences were not significant.

To test the hypothesis that the SOC-ADAP group is at increased risk for behavioral/emotional concerns, the Internalizing and Externalizing Index Scores from BASC/CBCLwere reviewed. Additional analyses were conducted on the Anxiety Scale, as this is

Table 4 Means (SD) Processing Speed for Combined Subgroup Comparisons.

MeasureProcessing

Efficiency n = 8Social Adaptation + Concept

Integration n = 22t- test

p < .05

Processing Speed Index1 90.87 (17.06) 84.95 (16.24) nsVerbal Fluency:

Letter Fluency 10.29 (2.62) 8.86 (3.4) nsCategory Fluency 12.00 (3.31) 8.77 (3.37) .04

1Processing Speed Index from WISC-III or WISC-IV.

Table 5 Means (SD) Visual Spatial Data for Combined Subgroup Comparisons.

MeasureConcept

Integration n = 14

Processing Efficiency + Social Adaptation n = 16 t-test p < .05

POI1/PRI 74.21 (8.3) 90.06 (11.85) .0003Rey-Osterrieth Figure:

Copy Organization 2.53 (1.8) 3.93 (2.28) nsVMI 69.5 (8.9) 81.4 (8.9) .0014Math Computation 79.71 (7.04) 95.87 (10.45) .0001

1POI/PRI: Perceptual Organization Index (WISC-III) / Perceptual Reasoning Index (WISC-IV).

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considered a consistent identified feature in the NLD literature (Pelletier et al., 2001;Rourke, 1995). Table 6 shows that the prediction of greater Internalization behaviors inthe SOC-ADAP group was not upheld on either the parent form, t(10.8) = −1.41, p < .18,or teacher rating scale, t(8.6) = 0.65, p < .53. Group differences, however, were found onthe Anxiety Scale in the expected direction on both the parent form, t(24.4) = −2.27 p <.03, and the teacher form, t(15.1) = 2.22, p < .04. The Externalizing Index showed theexpected direction on the parent form, t(17.2) = −2.36, p < .03, but not on the teacherform, t(13.7) = −1.60, p < .13.

DISCUSSION

This study is an exploratory investigation of differences in clinical presentation,adjustment, and outcome among children presenting for neuropsychological assessmentwho fulfilled criteria for NLD. Three groups were identified by means of a sorting strategyaccording to the diagnostic formulation provided by the clinician at the time of assess-ment. The prototypes for group membership were the lens through which we retrospec-tively viewed the larger cohort of children meeting NLD criteria.

This study is exploratory in two ways: First, it mines clinical practice data, and sec-ond, it investigates group differences within the construct of NLD. Clinical practice data area source of important insights into neurobehavioral functioning; a source that we believe isnot used enough. However, use of clinical practice data brings its own challenges. One ques-tion is the degree to which a clinician may shift his or her diagnostic thinking over time asnew knowledge is brought to bear—with the potential result that portraits painted later in aclinician’s career are different from those produced earlier. To counter this observation,however, we note that the unit of analysis (Bernstein, 2000; Bernstein & Waber, 2003) ofour clinical approach, the child, does not change quite so rapidly and that a clinician isgrounded diagnostically by the nature of the organism that is the focus of the clinical investi-gation. We also note that change in thinking may not be the direction of greatest potentialbias: In “judging under uncertainty”, clinicians are most vulnerable to essentially conserva-tive biases such as those of representativeness, availability, and/or anchoring and adjustment(Tversky & Kahneman, 1974). In this study, we addressed the possibility of diagnostic shiftby requiring the psychometric criteria for inclusion in the sample.

Table 6 Means (SD) for Social Adaptive Behavior for Combined Subgroup Comparisons.

MeasureSocial

Adaptation n = 7

Processing Efficiency + Concept

Integration n = 22 t-test p < .05

Internalizing IndexParent 63.85 (8.91) 58.31 (9.58) nsTeacher 54.0 (10.75) 56.88 (7.82) ns

Anxiety ScaleParent 66.71 (4.6) 60.09 (10.97) .03Teacher 53.14 (7.0) 60.88 (9.3) .04

Externalizing IndexParent 60.71 (6.9) 52.27 (11.4) .03Teacher 55.14 (8.2) 48.83 (10.24) ns

Note. Social Adaptive Behavior data derived from: CBCL or BASC.

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Sorting by diagnostic formulation permitted identification of the groups. In thisstudy the diagnostic formulation was based on both the review of neurobehavioral systemsand specific inclusionary psychometric data. Using these criteria, only 7% of well over400 children evaluated were identified, a small sample for analysis. In reviewing the totaldataset, however, it was evident that many more children presented with one of the behav-ioral styles described above but did not meet the psychometric criteria required for thisinitial study. There are at least three possible reasons for this. One, the age range is large:We may have missed children who earlier in their development had psychological testscores in the range required. Two, some children had clearly benefited from therapeuticinterventions. Three, some children may have the innate capacity to use compensatorystrategies to achieve age-expected academic scores without this influencing the behavioraland/or learning style that brought them to clinical attention. In future studies we plan toexplore the characteristics of children who present with the target behavioral profiles butwho do not fulfill the psychometric inclusion criteria, as well as the potential for changeover time in youngsters who have been followed throughout their childhood/adolescence(Fein, Dixon, Paul, & Levin, 2005).

Consistent with our overarching clinical practice approach, the subgroups werecharacterized according to the way in which they were experienced by the clinician and byothers in their world (see discussion of “looking at” versus “being with” as modes ofclinical observation; Bernstein, 2007). Thus, children in the PROC-EFF group are experi-enced as “wading through molasses.” Their processing of information is slow and poorlysynchronized, and their cognitive tempo is sluggish, undermining executive skills acrossthe board and derailing overall functioning. For many practicing clinicians the descriptionof the PROC-EFF group may prompt a query as to its relationship to Attention Deficit/Hyperactivity Disorder, Predominantly Inattentive (ADHD/PI; American PsychiatricAssociation, 1994) or a subset of ADHD/PI described as having a “sluggish cognitivetempo” (SCT; Carlson & Mann, 2002). We feel that the PROC-EFF group can be differ-entiated from individuals diagnosed with ADHD. Formally, only a subset of the popula-tion under study fulfilled diagnostic criteria for ADHD. Clinically, children in this groupdo not present with the broad organization problems or the distractibility described ofindividuals with ADHD. They do, however, present with more visual scanning deficits,fewer rehearsal strategies, and reduced working memory “capacity” for “holding informa-tion” (Baddeley & Hitch, 1974). It is also not clear how any of the groups characterizedhere may relate to developmental right hemisphere syndrome that, like NLD, may be toobroad a diagnostic categorization to guide tailored intervention strategies. The relationshipof right hemisphere dysfunction proposed as an explanatory variable both for the original NLDconstruct and for the ADD-without-hyperactivity diagnosis (Stefanatos & Wasserstein, 2001)requires continued exploration.

The CONC-INT group has been traditionally labeled in terms of “spatial processing”and, more frequently than not, as having “visual-spatial deficits.” We find the lattercharacterization problematic on three counts. First, visual-spatial deficits are typically“diagnosed” in terms of poor performance on psychological tests whose materials mayhave face validity (in that they involve visually and spatially represented stimuli) but donot necessarily have theoretically important construct or ecological validity. “Visual-spa-tial deficits” do not speak to whether one can perceive edges, orientation, etc., and then“bind” these into a higher percept, nor to whether one navigates successfully in the visu-ally and spatially extended environment. In what way do they have a visual-spatial deficit?What does this mean? How does it inform intervention (Burgess et al., 2006)? Second, it is

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not clear how the “visual-spatial deficits” explicate problems in managing stimulus over-load, in social functioning, or in math performance, all of which are seen in childrendescribed as having visual-spatial deficits. Third, visual-spatial deficits as a diagnosis istoo promiscuous to have any useful explanatory power. Children with a range of neurode-velopmental disorders do poorly on tests that are considered to index “visual-spatial func-tioning” as discussed above. But their underlying problem is not typically modalityspecific: Children with this type of presentation have trouble processing and managinginformation across the board. We believe that the latter is the core of the difficulty and thatthe tests that yield the “visual-spatial” label are more importantly construed as indexingfluid reasoning and the ability to generate an internal representation to guide solution onan independent basis. These children need not be lacking in knowledge and they are notskill-poor, but they are all too easily mired in detail and unable to discover the relationshipor pattern that captures meaning. Their problem is arguably one of thinking, rather than oflearning. Accordingly, we chose to capture what we see as a problem involving the inte-gration of streams of processing in the label Concept Integration.

The SOC-ADAP group was characterized in terms of ecological maladaptation.Social adaptation is a very broad construct that includes a wide variety of subcomponents(Blakemore, 2008; Yeates et al., 2007). The relevant behaviors are easily accessible to allindividuals who deal with the child, i.e., parents and teachers who typically provide awealth of observations to complement and validate those of the clinician. However, thesebehaviors are not easily assessed by currently available standardized tests. The clinician’sdiagnostic tools such as the BASC/CBCL, while important in the armamentarium, do notsubstitute for the information obtained through interview and observation. We propose theforegoing labels to our colleagues for critique, further investigation, and refinement.

Having identified these subgroups, we analyzed their psychometric data. The moststriking finding was the discrepancy in performance between the CONC-INT group ascompared to the PROC-EFF and SOC-ADAP groups. All three groups fulfilled the IQrequirement of VCI or POI/PRI > 70. Nonetheless, the CONC-INT group had substan-tially lower IQ scores than the other two and, indeed, performed significantly less welloverall.

We examined the data to determine if the low performance of the CONC-INT groupwas an artifact of age at time of testing. It was not. We were, however, unable to deter-mine whether the discrepancy between the CONC-INT group and the other two groupswas present throughout their academic career or developed over time. Given the depen-dence of IQ and achievement scores on success in school, an underlying deficit that limitsthe ability to acquire new learning in the same manner and the same rate as peers can beexpected to culminate in loss of IQ and achievement points over time. A hypothesis to beentertained is that a deeply embedded difficulty in developing and maintaining mentalrepresentations to guide performance is an index of significant developmental disruptionin the biological substrate that has pervasive impact on the ability to acquire knowledgeand skills.

The following specific hypotheses explored pertain to processing speed, visual-spa-tial deficits and social adaptation.

Processing Speed

As noted above, a slow cognitive tempo is very salient in a clinician’s interaction witha child and prompts consideration of the slow processing speed construct in interpreting the

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child’s behavior. Given the traditionally available tools, the clinician often looks to theWISC-PSI and measures such as the Verbal Fluency tasks to validate the clinical observa-tion. Use of either of these measures was noncontributory in our sample. Neither taskspeaks to the concept of processing speed as it reflects the time-dependent processesrequired for the basic scan-select-respond activity that is fundamental to Central NervousSystem (CNS) functioning, the focus of the research literature (Demaree, Frazier, &Johnson, 2008; R. Kail, 2008; R. V. Kail, 2008; Salthouse, Pink, & Tucker-Drob, 2008;Salthouse & Tucker-Drob, 2008). Processing speed needs to be indexed with appropriatetools such as reaction time measures (Jensen, 2006; Landau, Auerbach, Gross-Tsur, &Shalev, 2003). As understood neuropsychologically, however, the speed-of-processingconcept captures exactly the “wading through molasses” experience of working with thePROC-EFF child, a behavioral style that was missed by the traditional psychological mea-sures. These data provide further evidence that the construct of “processing speed” as cur-rently employed in clinical pediatric neuropsychology is in need of refinement. Targetedprocessing speed measures that can more directly index CNS information transfer ratesare badly needed (Schmiedek, Oberauer, Wilhelm, Suss, & Wittmann, 2007).

“Visual-Spatial” Processing

Following previous work in NLD, we examined the role of “visual-spatial” tasks.(We note, however, that this label is indiscriminately inclusive in the (neuro)psychologi-cal literature—running the gamut from visual-perceptual processing to navigation in spaceto copying/constructing of abstract geometric forms to academic skill products.) Here, weanalyzed only psychological tests that are part of the typical clinical assessment armamen-tarium, namely the VMI test, the ROCF, the POI/PRI, and Math Computation. The threetests/indexes whose scoring systems have been subjected to standardization procedures(VMI, POI/PRI, Math Computation) all performed similarly (and did no more than thestandardized IQ scores/indexes) in identifying the CONC-INT group as different from theother two. In contrast, the ROCF with its developmental scoring system did not. Allgroups scored poorly on the DSS-ROCF. The scoring system was sensitive to derailmentof the developmental trajectory common to all three groups but provided no specific dif-ferentiating information. Nor was group differentiation obtained by a clinical sorting strat-egy as employed in the original studies that led to the DSS-ROCF and in subsequentstudies with impaired children with surgically treated cardiac conditions (Bellinger, Bern-stein, Kirkwood, Rappaport, & Newburger, 2003).

Social Adaptation

The problems in social adaptation that are characteristic of the SOC-ADAP groupand are so clearly manifest in both home and school settings are easy to elicit by clinicalinterview and observation. They are not accessible to psychological tests; they are only“lightly” tapped by behavior-rating scales and are subject to rater bias. The BASC Exter-nalizing Scale did, however, prove sensitive to group differences in social functioning andin the expected direction: The SOC-ADAP group exhibited more maladaptive behaviorsthan the other two groups. This group difference did not reach significance on the Internal-izing scale, though there was a trend in the expected direction. Parents also rated thesechildren as showing significantly greater anxiety behaviors; a rating that was consistentwith clinician observations. The anxiety scale measurements differed across groups and

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reporters: The SOC-ADAP group was rated more anxious than the combined PROC-EFFand CONC-INT groups—by parents only. Contributors to this outcome that need to beexplored include: the role of differential experience in environments that differ markedlyin their structure, routines, and expectations and thus elicit different vulnerabilities orstrengths at different times; the possibility of limited teacher sensitivity to emotional sig-nals in individuals with social deficits (O’Callaghan, Fishbein, Calvanio, Grant, & Levine,2004); and/or the impact of symptoms, experience, or reaction formations of living in asocial world that one cannot process effectively (Palombo, 2001).

Two important methodological issues are raised by this study. First, in this initialinvestigation, the sample directly reflects the age range of clinical referral patterns: inthis case, from 7 to 16 years. This wide age range raises questions about the role of devel-opmental change in children’s behavioral repertoires. Chronological age reflects theamount of time that the organism has been in dynamic interaction with the environment.The quality and timing of the experiences resulting from this ongoing interaction influ-ence ongoing development in different ways. Behavioral capacities may be derailed atdifferent developmental epochs as new constellations of skills become available withmaturation and are elicited under changing experiential demands. This is not only a chal-lenge to an understanding of neurobehavioral development and its derailment by disease,disorder, and/or adverse experience but also has a potential impact on the construction ofdiagnostic classifications of behavioral conditions. How is this change incorporated intoa classificatory system? Understanding the dynamics of developmental change is not atrivial undertaking and one that is only just beginning to be explored in neuropsychology.Exploring such complex interactions for NLD in particular and neurobehavioral condi-tions in general will require theory-driven assessment strategies that are more preciselytargeted to developmental stage and level of ability—as well as appropriately large sam-ples. Not only explanatory theories but also the “action theories” that guide clinical work(Perkins, 2009) must be developmentally framed. Such theories can be expected to guidethe nature and timing of not only evaluation but also intervention strategies (Annaz,Karmiloff-Smith, & Thomas, 2008). This can be expected to be a major thrust in futureinvestigations.

The second issue relates to the use of qualitative and quantitative data in clinicaldiagnosis. This study highlights the inability of traditionally used standardized tests tocapture clinical observations that derive their validity from the clinical and developmentalresearch literature. Our findings, though exploratory, attest to the importance ofunderstanding the role of the clinician in the evaluation and interpretation of behavior(Bernstein & Weiler, 2000). Clinical expertise requires knowledge and tools, both concep-tual and practical. The conceptual tools of theory-driven models that organize the relevantdata and permit hypotheses to be generated and evaluated are the critical contribution ofthe clinician. For practical tools, clinicians have traditionally relied heavily on population-standardized tests and test batteries with known standards of reliability and validity. Thesemeasures, however, typically reflect broad processes and, to date, have been constructedaccording to models that are neither biologically nor developmentally framed. Asdevelopmental neuropsychologists, we have concerns about test-based methodologies;summary scores may be sensitive to age-related changes, but they have the potential toobscure changes or shifts in the underlying psychological processes that may vary differ-entially over development.

Limitations associated with this study largely follow from its exploratory nature.The sample is small in size limiting analyses within and across subgroups. In particular,

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we were unable to explore fully the impact of age and/or developmental change onsubgroup membership or the emergence of different behavioral domains such as language,executive function, or perceptual organization over the age range examined. Sources ofpotential sampling bias are present and include a restricted socioeconomic population(middle to upper middle income) and the possible overrepresentation of referrals ofchildren with NLD to the particular clinician. Also, while every effort was made to main-tain the spirit of Rourke’s (1995) original inclusion criteria, the necessity for clinical prac-tice to be based on the most current assessment tools may limit the generalizability of ourstatistical findings.

Future Directions

As pediatric clinicians, our primary concern is that the theoretical framework guid-ing assessment leads to principled intervention strategies that allow children to optimizetheir developmental progress in both social-adaptive and academic areas. We feel stronglythat children in the subgroups described above differ in their approach to, and adjustmentin, the world. They are poorly served by a single diagnostic label that lumps them alltogether. Indeed, many investigators have remained unconvinced that NLD is a unitarydisorder and/or that it can be straightforwardly explained by reference to a specific neuro-biological substrate. Nor is it clear that a subtype analysis approach would provide animproved account of the clinical phenomenology or be an optimal description of theunderlying causation. We believe that it is important to determine whether a more appro-priate characterization of this group of children would be one that identifies more than onecoherent disorder as the basis for clinical diagnosis. Characterizations such as Processingspeed/efficiency disorder, Concept integration disorder, or Social adaptation disorderthat respond to increasing knowledge about the neural substrate supporting the behaviorsin question may ultimately prove useful in generating hypotheses to guide investigationsof connectivity via modern neuroimaging techniques.

Finally, the particular presentations of these children merit diagnostic labels thatrespond to their specific behavioral profiles and shape the interventions that are mostlikely to promote their developmental progress. Finding the appropriate diagnostic labels,however, is not a trivial undertaking. Given the advances in neuropsychological theoryand concepts, a diagnostic labeling system based on the face-valid descriptions of psycho-logical tests is not acceptable. The appropriate diagnostic nosology should be one thatresponds to neuropsychological concepts and processes. However, agreement aboutdeficits in underlying processes may be difficult to achieve among researchers and clini-cians with different theoretical perspectives. Our preferred approach—characterizing adisorder in terms of its adaptive consequences for the individual—worked for the childwith problems in social adaptation but proved much harder to formulate for the child“wading through molasses” or the one who “learns but does not think.” A combination ofthe underlying-process and adaptation approaches is less than ideal, however, in that it isvulnerable to the potential biases of description at different levels of analysis. A formaleffort to reach a consensus in this area would be a valuable contribution of the profes-sional neuropsychological community towards the better identification of, and improvedinterventions for, these groups of children.

Original manuscript received May 3, 2009Revised manuscript accepted November 11, 2009

First published online June 28, 2010

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A Practice-Based Approach to Group Identification in Nonverbal Learning Disorders

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