Brain Network Theory Can Predict Whether Neuropsychological Outcomes Will Differ from Clinical Expectations

Volumetric MRI Findings in Mild Traumatic Brain Injury (mTBI) and Neuropsychological Outcome

Region of interest (ROI) volumetric assessment has become a standard technique in quantitative neuroimaging. ROI volume is thought to represent a coarse proxy for making inferences about the structural integrity of a brain region when compared to normative values representative of a healthy sample, adjusted for age and various demographic factors. This review focuses on structural volumetric analyses that have been performed in the study of neuropathological effects from mild traumatic brain injury (mTBI) in relation to neuropsychological outcome. From a ROI perspective, the probable candidate structures that are most likely affected in mTBI represent the target regions covered in this review. These include the corpus callosum, cingulate, thalamus, pituitary-hypothalamic area, basal ganglia, amygdala, and hippocampus and associated structures including the fornix and mammillary bodies, as well as whole brain and cerebral cortex along with the cerebellum. Ventricular volumetrics are also reviewed as an indirect assessment of parenchymal change in response to injury. This review demonstrates the potential role and limitations of examining structural changes in the ROIs mentioned above in relation to neuropsychological outcome. There is also discussion and review of the role that post-traumatic stress disorder (PTSD) may play in structural outcome in mTBI. As emphasized in the conclusions, structural volumetric findings in mTBI are likely just a single facet of what should be a multimodality approach to image analysis in mTBI, with an emphasis on how the injury damages or disrupts neural network integrity. The review provides an historical context to quantitative neuroimaging in neuropsychology along with commentary about future directions for volumetric neuroimaging research in mTBI.

A Hierarchical Graph Learning Model for Brain Network Regression Analysis

Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.

Posterior Cortical Cognitive Deficits Are Associated With Structural Brain Alterations in Mild Cognitive Impairment in Parkinson's Disease

Context: Cognitive impairments are common in patients with Parkinson's disease (PD) and are heterogeneous in their presentation. The "dual syndrome hypothesis" suggests the existence of two distinct subtypes of mild cognitive impairment (MCI) in PD: a frontostriatal subtype with predominant attentional and/or executive deficits and a posterior cortical subtype with predominant visuospatial, memory, and/or language deficits. The latter subtype has been associated with a higher risk of developing dementia. Objective: The objective of this study was to identify structural modifications in cortical and subcortical regions associated with each PD-MCI subtype. Methods: One-hundred and fourteen non-demented PD patients underwent a comprehensive neuropsychological assessment as well as a 3T magnetic resonance imaging scan. Patients were categorized as having no cognitive impairment (n = 41) or as having a frontostriatal (n = 16), posterior cortical (n = 25), or a mixed (n = 32) MCI subtype. Cortical regions were analyzed using a surface-based Cortical thickness (CTh) method. In addition, the volumes, shapes, and textures of the caudate nuclei, hippocampi, and thalami were studied. Tractometric analyses were performed on associative and commissural white matter (WM) tracts. Results: There were no between-group differences in volumetric measurements and cortical thickness. Shape analyses revealed more abundant and more extensive deformations fields in the caudate nuclei, hippocampi, and thalami in patients with posterior cortical deficits compared to patients with no cognitive impairment. Decreased fractional anisotropy (FA) and increased mean diffusivity (MD) were also observed in the superior longitudinal fascicle, the inferior fronto-occipital fascicle, the striato-parietal tract, and the anterior and posterior commissural tracts. Texture analyses showed a significant difference in the right hippocampus of patients with a mixed MCI subtype. Conclusion: PD-MCI patients with posterior cortical deficits have more abundant and more extensive structural alterations independently of age, disease duration, and severity, which may explain why they have an increased risk of dementia.

Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs

Hubs are highly connected brain regions important for coordinating processing in brain networks. It is unclear, however, which measures of network "hubness" are most useful in identifying brain regions critical to human cognition. We tested how closely two measures of hubness-edge density and participation coefficient, derived from white and gray matter, respectively-were associated with general cognitive impairment after brain damage in two large cohorts of patients with focal brain lesions (N = 402 and 102, respectively) using cognitive tests spanning multiple cognitive domains. Lesions disrupting white matter regions with high edge density were associated with cognitive impairment, whereas lesions damaging gray matter regions with high participation coefficient had a weaker, less consistent association with cognitive outcomes. Similar results were observed with six other gray matter hubness measures. This suggests that damage to densely connected white matter regions is more cognitively impairing than similar damage to gray matter hubs, helping to explain interindividual differences in cognitive outcomes after brain damage.

Preschool Executive Function Predicts Childhood Resting-State Functional Connectivity and Attention-Deficit/Hyperactivity Disorder and Depression

BACKGROUND

Measures of executive function (EF), such as the Behavior Rating Inventory of Executive Function, distinguish children with attention-deficit/hyperactivity disorder (ADHD) from control subjects, but less work has examined relationships to depression or brain network organization. This study examined whether early childhood EF predicted new onset or worsening of ADHD and/or depression and examined how early childhood EF related to functional connectivity of brain networks at school age.

METHODS

Participants included 247 children who were enrolled at 3 to 6 years of age from a prospective study of emotion development. The Behavior Rating Inventory of Executive Function Global Executive Composite score was used as the measure of EF in early childhood to predict ADHD and depression diagnoses and symptoms across school age. Resting-state functional magnetic resonance imaging network analyses examined global efficiency in the frontoparietal, cingulo-opercular, salience, and default mode networks and six "hub" seed regions selected to examine between-network connectivity.

RESULTS

Early childhood EF predicted new onset and worsening of ADHD and depression symptoms across school age. Greater EF deficits in preschool predicted increased global efficiency in the salience network and altered connectivity with four regions for the dorsal anterior cingulate cortex hub and one region with the insula hub at school age. This altered connectivity was related to increasing ADHD and depression symptoms.

CONCLUSIONS

Early executive deficits may be an early common liability for risk of developing ADHD and/or depression and were associated with altered functional connectivity in networks and hub regions relevant to executive processes. Future work could help clarify whether specific EF deficits are implicated in the development of both disorders.

Neuropsychology and cognitive neuroscience in the fMRI era: A recapitulation of localizationist and connectionist views

OBJECTIVE

We highlight the past 25 years of cognitive neuroscience and neuropsychology, focusing on the impact to the field of the introduction in 1992 of functional MRI (fMRI).

METHOD

We reviewed the past 25 years of literature in cognitive neuroscience and neuropsychology, focusing on the relation and interplay of fMRI studies and studies utilizing the "lesion method" in human participants with focal brain damage.

RESULTS

Our review highlights the state of localist/connectionist research debates in cognitive neuroscience and neuropsychology circa 1992, and details how the introduction of fMRI into the field at that time catalyzed a new wave of efforts to map complex human behavior to specific brain regions. This, in turn, eventually evolved into many studies that focused on networks and connections between brain areas, culminating in recent years with large-scale investigations such as the Human Connectome Project.

CONCLUSIONS

We argue that throughout the past 25 years, neuropsychology-and more precisely, the "lesion method" in humans-has continued to play a critical role in arbitrating conclusions and theories derived from inferred patterns of local brain activity or wide-spread connectivity from functional imaging approaches. We conclude by highlighting the future for neuropsychology in the context of an increasingly complex methodological armamentarium. (PsycINFO Database Record

Dynamic Hippocampal and Prefrontal Contributions to Memory Processes and Representations Blur the Boundaries of Traditional Cognitive Domains

The hippocampus has long been known to be a critical component of the memory system involved in the formation and use of long-term declarative memory. However, recent findings have revealed that the reach of hippocampal contributions extends to a variety of domains and tasks that require the flexible use of cognitive and social behavior, including domains traditionally linked to prefrontal cortex (PFC), such as decision-making. In addition, the prefrontal cortex (PFC) has gained traction as a necessary part of the memory system. These findings challenge the conventional characterizations of hippocampus and PFC as being circumscribed to traditional cognitive domains. Here, we emphasize that the ability to parsimoniously account for the breadth of hippocampal and PFC contributions to behavior, in terms of memory function and beyond, requires theoretical advances in our understanding of their characteristic processing features and mental representations. Notably, several literatures exist that touch upon this issue, but have remained disjointed because of methodological differences that necessarily limit the scope of inquiry, as well as the somewhat artificial boundaries that have been historically imposed between domains of cognition. In particular, this article focuses on the contribution of relational memory theory as an example of a framework that describes both the representations and processes supported by the hippocampus, and further elucidates the role of the hippocampal–PFC network to a variety of behaviors.