The evolution of cognitive models: From neuropsychology to neuroimaging and back

Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition

Traditional models of reading lack a realistic simulation of the early visual processing stages, taking input in the form of letter banks and predefined line segments, making them unsuitable for modeling early brain responses. We used variations of the VGG-11 convolutional neural network (CNN) to create models of visual word recognition that starts from the pixel-level and performs the macro-scale computations needed for the detection and segmentation of letter shapes to word-form identification of large vocabulary of 10k Finnish words, regardless of letter size, shape, or rotation. The models were evaluated based on an existing magnetoencephalography (MEG) study where participants viewed regular words, pseudowords, noise-embedded words, symbol strings, and consonant strings. The original images used in the study were presented to the models and the activity in the layers was compared to MEG evoked response amplitudes. Through a few alterations to make the network more biologically plausible, we found an CNN architecture that can correctly simulate the behavior of three prominent responses, namely the type I (early visual response), type II (the 'letter string' response), and the N400m. In conclusion, starting a model of reading with convolution-and-pooling steps enables the flexibility and realism crucial for a direct model-to-brain comparison.

What does the Developmental Neuropsychological Assessment-II (NEPSY-II) measure in children ages 7 to 12? A structural and psychometric analysis

The Developmental Neuropsychological Assessment - II (NEPSY-II) is a widely used assessment battery in pediatric settings, but its internal structure has not been adequately examined. This study employed a rational, empirical approach to examine the construct validity of 23 NEPSY-II subtest scores from children ages 7-12 (M = 9.99, SD = 2.76) in the NEPSY-II norming sample (N = 600; 50% girls). Competing higher-order models based on prior research, hypothesized NEPSY-II domains, and conceptual subtest classifications were evaluated via confirmatory factor analysis and a sequential approach to model comparisons. The results supported the multidimensionality of NEPSY-II subtests and the organization of subtests by hypothesized neuropsychological domains. The best fitting model included a general factor and four first-order factors. Factor loadings from the general factor to first-order factors were very strong. However, general factor loadings for most subtests were less than .50 (range = .21-.69, M = .44), and domain-specific effects for all subtests, independent of the general factor, were even lower (range = .00-.45, M = .44). Interestingly, all subtests demonstrated strong subtest-specific effects, but it is not clear what construct(s) the subtest-specific effects represent. Findings support NEPSY-II authors' emphasis on subtest-level interpretations rather than composite-level interpretations and highlight that NEPSY-II subtest scores should be interpreted carefully and with caution.

Comparative analysis of Voxel-based morphometry using T1 and T2-weighted magnetic resonance imaging to explore the relationship between brain structure and cognitive abilities

Voxel-based morphometry (VBM) of T1-weighted (T1-w) magnetic resonance imaging (MRI) is primarily used to study the association of brain structure with cognitive functions. However, in theory, T2-weighted (T2-w) MRI could also be used in VBM studies because of its sensitivity to pathology and tissue changes. We aimed to compare the T1-w and T2-w images to study brain structures in association with cognitive abilities. VBM analysis was applied to T1-w and T2-w MRI data of 120 healthy participants aged 20 to 40. The MRI data was collected using a 3T machine, and it was analyzed with CAT12 to extract maps of Gray matter(GM). We used six cognitive tasks to assess cognitive abilities, including the balloon analog risk task (BART), block design, forward and backward digit span (FDST and BDST), and trail-making tasks A and B. Compared to T2-w, T1-w data showed more brain voxels in the BART, block design, FDST, TMT-A, and TMT-B tasks. However, T2-w imaging identified a greater number of voxels in the BDST. T1-w images identified more correlated brain regions with cognitive scores in the FDST, TMT-A, and B tasks than T2-w. In BART and Block design tasks, both methods revealed the same number of correlated regions, and T2-w just showed more regions than T1-w in the BDST. Findings revealed distinct patterns of sensitivity between T1-w and T2-w imaging in detecting brain regions associated with cognition. The two approaches demonstrated different strengths in identifying areas correlated with cognitive abilities. This study provides valuable guidance for selecting appropriate methods for identifying the optimal approach for detecting brain regions that exhibit the strongest correlations with cognitive abilities.

Development, validity, and reliability of Neural Circuits Questionnaire (NCQ)

After a prolonged period of using the cortico-centric models of brain function, scientists developed a parallel perspective with an emphasis on all neural structures of the brain. Therefore, the present study aims to create a valid and reliable assessment based on the parallel perspective of brain function. First, a basic assessment was made. Then, a sample size of 183 participants was recruited from graduates and undergraduates at the Ferdowsi University of Mashhad over a 2-year period (2020 and 2021). The measures were the Neural Circuits Questionnaire (NCQ) and the Demographic Data Questionnaire. Data analyses were performed by using exploratory factor analysis. Using a receiver operating characteristic (ROC) curve, cutting points for the quality index of the questionnaire were calculated. The results showed that the questionnaire has a three-factor structure and a Cronbach's alpha of 0.617. Based on the ROC curve, the valid range for the quality index is 10.5-15.5. The cerebro-cerebellar index is higher for Ph.D. students than for students with lower degrees (F = 8.60, p = 0.001). In conclusion, it appears that the Neural Circuits Questionnaire is a valid and reliable questionnaire to assess cognitive function following the parallel perspective of brain function.

Decomposing Neural Circuit Function into Information Processing Primitives

It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.

Neural network process simulations support a distributed memory system and aid design of a novel computer adaptive digital memory test for preclinical and prodromal Alzheimer's disease

OBJECTIVE

Growing evidence supports the importance of learning as a central deficit in preclinical/prodromal Alzheimer's disease. The aims of this study were to conduct a series of neural network simulations to develop a functional understanding of a distributed, nonmodular memory system that can learn efficiently without interference. This understanding is applied to the development of a novel digital memory test.

METHOD

Simulations using traditional feed forward neural network architectures to learn simple logic problems are presented. The simulations demonstrate three limitations: (a) inefficiency, (b) an inability to learn problems consistently, and (c) catastrophic interference when given multiple problems. A new mirrored cascaded architecture is introduced to address these limitations, with support provided by a series of simulations.

RESULTS

The mirrored cascaded architecture demonstrates efficient and consistent learning relative to feed forward networks but also suffers from catastrophic interference. Addition of context values to add the capability of distinguishing features as part of learning eliminates the problem of interference in the mirrored cascaded, but not the feed forward, architectures.

CONCLUSIONS

A mirrored cascaded architecture addresses the limitations of traditional feed forward neural networks, provides support for a distributed memory system, and emphasizes the importance of context to avoid interference. These process models contributed to the design of a digital computer-adaptive word list learning test that places maximum stress on the capability to distinguish specific episodes of learning. Process simulations provide a useful method of testing models of brain function and contribute to new approaches to neuropsychological assessment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Formal Neuropsychological Testing: Test Batteries, Interpretation, and Added Value in Practice

Neuropsychologists evaluate patients for cognitive decline and dementia, using validated psychometric tests, along with behavioral observation, record review, clinical interview, and information about psychological functioning, to evaluate brain-behavior relationships and aid in differential diagnosis and treatment planning. Also considered are premorbid functioning, education, sex, socioeconomic status, primary language, culture, and race-related health disparities when selecting tests, interpreting performance, and providing a diagnostic impression. Neuropsychologists provide diagnostic clarity, explain symptoms and likely disease course to patients and family members, and assist the family with future planning, behavioral management strategies, and ways to mitigate caregiver burden.

Context-sensitive computational mechanistic explanation in cognitive neuroscience

Mainstream cognitive neuroscience aims to build mechanistic explanations of behavior by mapping abilities described at the organismal level via the subpersonal level of computation onto specific brain networks. We provide an integrative review of these commitments and their mismatch with empirical research findings. Context-dependent neural tuning, neural reuse, degeneracy, plasticity, functional recovery, and the neural correlates of enculturated skills each show that there is a lack of stable mappings between organismal, computational, and neural levels of analysis. We furthermore highlight recent research suggesting that task context at the organismal level determines the dynamic parcellation of functional components at the neural level. Such instability prevents the establishment of specific computational descriptions of neural function, which remains a central goal of many brain mappers - including those who are sympathetic to the notion of many-to-many mappings between organismal and neural levels. This between-level instability presents a deep epistemological challenge and requires a reorientation of methodological and theoretical commitments within cognitive neuroscience. We demonstrate the need for change to brain mapping efforts in the face of instability if cognitive neuroscience is to maintain its central goal of constructing computational mechanistic explanations of behavior; we show that such explanations must be contextual at all levels.

From Action to Cognition: Neural Reuse, Network Theory and the Emergence of Higher Cognitive Functions

The aim of this article is to discuss the logic and assumptions behind the concept of neural reuse, to explore its biological advantages and to discuss the implications for the cognition of a brain that reuses existing circuits and resources. We first address the requirements that must be fulfilled for neural reuse to be a biologically plausible mechanism. Neural reuse theories generally take a developmental approach and model the brain as a dynamic system composed of highly flexible neural networks. They often argue against domain-specificity and for a distributed, embodied representation of knowledge, which sets them apart from modular theories of mental processes. We provide an example of reuse by proposing how a phylogenetically more modern mental capacity (mental rotation) may appear through the reuse and recombination of existing resources from an older capacity (motor planning). We conclude by putting arguments into context regarding functional modularity, embodied representation, and the current ontology of mental processes.

Neuro-Clinical Signatures of Language Impairments after Acute Stroke: A VBQ Analysis of Quantitative Native CT Scans

OBJECTIVES

Ischemic stroke affects language production and/or comprehension and leads to devastating long-term consequences for patients and their families. Previous studies have shown that neuroimaging can increase our knowledge of the basic mechanisms of language recovery. Currently, models for predicting patients' outcomes have limited use in the clinic for the evaluation and optimization of rehabilitative strategies mostly because that are often based on high-resolution magnetic resonance imaging (MRI) data, which are not always possible to carry out in the clinical routine. Here, we investigate the use of Voxel-Based Morphometry (VBM), multivariate modelling and native Computed Tomography (nCT) scans routinely acquired in the acute stage of stroke for identifying biological signatures that explicate the relationships between brain anatomy and types of impairments.

METHODS

80 stroke patients and 30 controls were included. nCT-scans were acquired in the acute ischemia stage and bedside clinical assessment from board-certified neurologist based on the NIH stroke scale. We use a multivariate Principal Component Analyses (PCA) to identify the brain signatures group the patients according to the presence or absence of impairment and identify the association between local Grey Matter (GM) and White Matter (WM) nCT values with the presence or absence of the impairment.

RESULTS

Individual patient's nCT scans were compared to a group of controls' with no radiological signs of stroke to provide an automated delineation of the lesion. Consistently across the whole group the regions that presented significant difference GM and WM values overlap with known areas that support language processing.

CONCLUSION

In summary, the method applied to nCT scans performed in the acute stage of stroke provided robust and accurate information about brain lesions' location and size, as well as quantitative values. We found that nCT and VBQ analyses are effective for identifying neural signatures of concomitant language impairments at the individual level, and neuroanatomical maps of aphasia at the population level. The signatures explicate the neurophysiological mechanisms underlying aetiology of the stroke. Ultimately, similar analyses with larger cohorts could lead to a more integrated multimodal model of behaviour and brain anatomy in the early stage of ischemic stroke.

Neuro-Clinical Signatures of Language Impairments: A Theoretical Framework for Function-to-structure Mapping in Clinics

In the past decades, neuroscientists and clinicians have collected a considerable amount of data and drastically increased our knowledge about the mapping of language in the brain. The emerging picture from the accumulated knowledge is that there are complex and combinatorial relationships between language functions and anatomical brain regions. Understanding the underlying principles of this complex mapping is of paramount importance for the identification of the brain signature of language and Neuro-Clinical signatures that explain language impairments and predict language recovery after stroke. We review recent attempts to addresses this question of language-brain mapping. We introduce the different concepts of mapping (from diffeomorphic one-to-one mapping to many-to-many mapping). We build those different forms of mapping to derive a theoretical framework where the current principles of brain architectures including redundancy, degeneracy, pluri-potentiality and bow-tie network are described.

Support vector machine based aphasia classification of transcranial magnetic stimulation language mapping in brain tumor patients

Repetitive TMS (rTMS) allows for non-invasive and transient disruption of local neuronal functioning. We used machine learning approaches to assess whether brain tumor patients can be accurately classified into aphasic and non-aphasic groups using their rTMS language mapping results as input features. Given that each tumor affects the subject-specific language networks differently, resulting in heterogenous rTMS functional mappings, we propose the use of machine learning strategies to classify potential patterns of rTMS language mapping results. We retrospectively included 90 patients with left perisylvian world health organization (WHO) grade II-IV gliomas that underwent presurgical navigated rTMS language mapping. Within our cohort, 29 of 90 (32.2%) patients suffered from at least mild aphasia as shown in the Aachen Aphasia Test based Berlin Aphasia Score (BAS). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each stimulated cortical area (28 regions of interest, ROI) by automated anatomical labeling parcellation (AAL3) and IIT. We used a support vector machine (SVM) to classify significant areas in relation to aphasia. After feeding the ROIs into the SVM model, it revealed that in addition to age (w = 2.98), the ERs of the left supramarginal gyrus (w = 3.64), left inferior parietal gyrus (w = 2.28) and right pars triangularis (w = 1.34) contributed more than other features to the model. The model's sensitivity was 86.2%, the specificity was 82.0%, the overall accuracy was 85.5% and the AUC was 89.3%. Our results demonstrate an increased vulnerability of right inferior pars triangularis to rTMS in aphasic patients due to left perisylvian gliomas. This finding points towards a functional relevant involvement of the right pars triangularis in response to aphasia. The tumor location feature, specified by calculating overlaps with white and grey matter atlases, did not affect the SVM model. The left supramarginal gyrus as a feature improved our SVM model the most. Additionally, our results could point towards a decreasing potential for neuroplasticity with age.

Paradigm Shift Toward Digital Neuropsychology and High-Dimensional Neuropsychological Assessments: Review

Neuropsychologists in the digital age have increasing access to emerging technologies. The National Institutes of Health (NIH) initiatives for behavioral and social sciences have emphasized these developing scientific and technological potentials (eg, novel sensors) for augmented characterization of neurocognitive, behavioral, affective, and social processes. Perhaps these innovative technologies will lead to a paradigm shift from disintegrated and data-poor behavioral science to cohesive and data-rich science that permits improved translation from bench to bedside. The 4 main advances influencing the scientific priorities of a recent NIH Office of Behavioral and Social Sciences Research strategic plan include the following: integration of neuroscience into behavioral and social sciences, transformational advances in measurement science, digital intervention platforms, and large-scale population cohorts and data integration. This paper reviews these opportunities for novel brain-behavior characterizations. Emphasis is placed on the increasing concern of neuropsychology with these topics and the need for development in these areas to maintain relevance as a scientific discipline and advance scientific developments. Furthermore, the effects of such advancements necessitate discussion and modification of training as well as ethical and legal mandates for neuropsychological research and praxes.

Extended Reality for the Clinical, Affective, and Social Neurosciences

Brain science research often involves the use of low-dimensional tools and stimuli that lack several of the potentially valuable features of everyday activities and interactions. Although this research has provided important information about cognitive, affective, and social processes for both clinical and nonclinical populations, there is growing interest in high-dimensional simulations that extend reality. These high-dimensional simulations involve dynamic stimuli presented serially or concurrently to permit the assessment and training of perceivers' integrative processes over time. Moreover, high-dimensional simulation platforms can contextually restrain interpretations of cues about a target's internal states. Extended reality environments extend assessment and training platforms that balance experimental control with emotionally engaging background narratives aimed at extending the affective experience and social interactions. Herein, we highlight the promise of extended reality platforms for greater ecological validity in the clinical, affective, and social neurosciences.

Common Neural System for Sentence and Picture Comprehension Across Languages: A Chinese-Japanese Bilingual Study

While common semantic representations for individual words across languages have been identified, a common meaning system at sentence-level has not been determined. In this study, fMRI was used to investigate whether an across-language sentence comprehension system exists. Chinese–Japanese bilingual participants (n = 32) were asked to determine whether two consecutive stimuli were related (coherent) or not (incoherent) to the same event. Stimuli were displayed with three different modalities (Chinese written sentences, Japanese written sentences, and pictures). The behavioral results showed no significant difference in accuracy and response times among the three modalities. Multi-voxel pattern analysis (MVPA) of fMRI data was used to classify the semantic relationship (coherent or incoherent) across the stimulus modalities. The classifier was first trained to determine coherency within Chinese sentences and then tested with Japanese sentences, and vice versa. A whole-brain searchlight analysis revealed significant above-chance classification accuracy across Chinese and Japanese sentences in the supramarginal gyrus (BA 40), extending into the angular gyrus (BA 39) as well as the opercular (BA 44) and triangular (BA 45) parts of the inferior frontal gyrus in the left hemisphere (cluster-level FWE corrected p < 0.05). Significant above-chance classification accuracy was also found across Japanese sentences and pictures in the supramarginal (BA 40) and angular gyrus (BA 39). These results indicate that a common meaning system for sentence processing across languages and modalities exists, and it involves the left inferior parietal gyrus.

The Long View of Language Localization

For British neurologists, one case was considered to represent significant evidence regarding the organization of language in the brain in the second half of the 19th century. The interpretation of its significance was based on repeated standard clinical assessment of behavioral deficits, the use of a psychological model of processing, and lesion localization to inform understanding of clinic-pathological correlation. The aphasic deficits experienced by a single case were observed and recorded by London neurologist Henry Charlton Bastian (1837-1915) over a period of 18 years and used as a demonstration of clinico-pathological reasoning regarding language function. This case was well documented in many of Bastian's publications; presented in teaching demonstrations; included in discussions at medical society meetings and public lectures; and reported widely in the medical press. When this patient died, the autopsy findings were added to the extensive record of his language deficits. Some aspects of the size and site of the lesion were consistent with Bastian's clinical predictions arising from his model of language processing, while others presented more of a paradox. This single case was a significant source of discussion and reflection in the medical community throughout the second half of the 19th century. Examination of various interpretations of this case reveal the assumptions regarding the functional architecture of language processing and more general theoretical considerations of how evidence from cases of acquired neurogenic aphasia can be employed in developing such models. This long view into a historical case sheds light on the challenges of clinic-pathological correlation methods in the understanding of localization of language functions which remain today.

National Institutes of Health initiatives for advancing scientific developments in clinical neuropsychology

OBJECTIVE

The current review briefly addresses the history of neuropsychology as a context for discussion of developmental milestones that have advanced the profession, as well as areas where the progression has lagged. More recently in the digital/information age, utilization and incorporation of emerging technologies has been minimal, which has stagnated ongoing evolution of the practice of neuropsychology despite technology changing many aspects of daily living. These authors advocate for embracing National Institutes of Health (NIH) initiatives, or interchangeably referred to as transformative opportunities, for the behavioral and social sciences. These initiatives address the need for neuropsychologists to transition from fragmented and data-poor approaches to integrated and data-rich scientific approaches that ultimately improve translational applications. Specific to neuropsychology is the need for the adoption of novel means of brain-behavior characterizations.

METHOD

Narrative review Conclusions: Clinical neuropsychology has reached a developmental plateau where it is ready to embrace the measurement science and technological advances which have been readily adopted by the human neurosciences. While there are ways in which neuropsychology is making inroads into these areas, a great deal of growth is needed to maintain relevance as a scientific discipline (see Figures 1, 2, and 3) consistent with NIH initiatives to advance scientific developments. Moreover, implications of such progress require discussion and modification of training, ethical, and legal mandates of the practice of neuropsychology.

Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension

Recent decades have ushered in tremendous progress in understanding the neural basis of language. Most of our current knowledge on language and the brain, however, is derived from lab-based experiments that are far removed from everyday language use, and that are inspired by questions originating in linguistic and psycholinguistic contexts. In this paper we argue that in order to make progress, the field needs to shift its focus to understanding the neurobiology of naturalistic language comprehension. We present here a new conceptual framework for understanding the neurobiological organization of language comprehension. This framework is non-language-centered in the computational/neurobiological constructs it identifies, and focuses strongly on context. Our core arguments address three general issues: (i) the difficulty in extending language-centric explanations to discourse; (ii) the necessity of taking context as a serious topic of study, modeling it formally and acknowledging the limitations on external validity when studying language comprehension outside context; and (iii) the tenuous status of the language network as an explanatory construct. We argue that adopting this framework means that neurobiological studies of language will be less focused on identifying correlations between brain activity patterns and mechanisms postulated by psycholinguistic theories. Instead, they will be less self-referential and increasingly more inclined towards integration of language with other cognitive systems, ultimately doing more justice to the neurobiological organization of language and how it supports language as it is used in everyday life.