Longitudinal changes in ventricle volume following pediatric traumatic brain injury : predictors of cognitive function one year later

Ventricular enlargement in pediatric TBI is a common observation in clinical practice, yet volumetric studies of ventricle volume are sparse. In this study, MRIs and neuropsychological testing were performed on children who had sustained a traumatic brain injury (n=38) and control children (n=34) between the ages of six and eighteen years. Children with traumatic brain injury (mean GCS=9.0) were evaluated at two time points (time one: mean=55 days post injury, n=38; time two: mean=324 days post injury, n=21). Results revealed that ventricular enlargement and deficits in cognitive function were present in the TBI group during both the semi-acute (\u3c6 months post injury) and chronic (\u3e6 months post injury) phases of injury (as compared to typically developing controls). Furthermore, ventricular enlargement and cognitive function during the semi-acute phase of injury were correlated with initial injury severity (as measured by the Glasgow Coma Scale). Additionally, lateral ventricle volume and third ventricle volume were correlated with the executive composite during the semi-acute phase of injury. During the chronic phase of injury, only third ventricle volume (not lateral ventricle volume) was significantly correlated with the executive and memory composites. Examination of the longitudinal data revealed that third ventricle volume significantly decreased between times one and two, while cognitive function significantly improved. Finally, results of regression analyses revealed that third ventricle enlargement at time one was the best predictor of cognitive function at time two. Our results reiterate the importance of longitudinal designs in pediatric TBI and indicate the utility of third ventricle volume in the semi-acute phase of injury as a predictor of neuropsychological function

Cortical Dynamics of Language

The human capability for fluent speech profoundly directs inter-personal communication and, by extension, self-expression. Language is lost in millions of people each year due to trauma, stroke, neurodegeneration, and neoplasms with devastating impact to social interaction and quality of life. The following investigations were designed to elucidate the neurobiological foundation of speech production, building towards a universal cognitive model of language in the brain. Understanding the dynamical mechanisms supporting cortical network behavior will significantly advance the understanding of how both focal and disconnection injuries yield neurological deficits, informing the development of therapeutic approaches

Feature selective temporal prediction of Alzheimer’s disease progression using hippocampus surface morphometry

IntroductionPrediction of Alzheimer’s disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.MethodsPrevious work has shown that a multi‐task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer‐based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor‐based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.ResultsWe combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.ConclusionsBy combining the power of the cFSGL multi‐task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.In this work, we present our results of using machine learning to predict temporal behavior changes in Alzheimers Disease using entire topological feature maps of the hippocampus surface (2100 feature points). Our paper demonstrates that it is possible to use an entire topological map instead of just imaging derived volumetric measurements for predicting behavioral changes. We compare these results with previous results using only volumetric MR imaging features (309 features points) and show through repeated cross‐validation rounds that we are able to get better predictive power.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/1/brb3733_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/2/brb3733.pd

Addressing Confounding in Predictive Models with an Application to Neuroimaging

Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease effects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples

Bipolar disorder and neurophysiologic mechanisms

Recent studies have suggested that some variants of bipolar disorder (BD) may be due to hyperconnectivity between orbitofrontal (OFC) and temporal pole (TP) structures in the dominant hemisphere. Some initial MRI studies noticed that there were corpus callosum abnormalities within specific regional areas and it was hypothesized that developmentally this could result in functional or effective connectivity changes within the orbitofrontal-basal ganglia-thalamocortical circuits. Recent diffusion tensor imaging (DTI) white matter fiber tractography studies may well be superior to region of interest (ROI) DTI in understanding BD. A “ventral semantic stream” has been discovered connecting the TP and OFC through the uncinate and inferior longitudinal fasciculi and the elusive TP is known to be involved in theory of mind and complex narrative understanding tasks. The OFC is involved in abstract valuation in goal and sub-goal structures and the TP may be critical in binding semantic memory with person–emotion linkages associated with narrative. BD patients have relative attenuation of performance on visuoconstructional praxis consistent with an atypical localization of cognitive functions. Multiple lines of evidence suggest that some BD alleles are being selected for which could explain the enhanced creativity in higher-ability probands. Associations between ROI’s that are not normally connected could explain the higher incidence of artistic aptitude, writing ability, and scientific achievements among some mood disorder subjects

The Cognitive and Neural Basis for Apathy in Frontotemporal Degeneration

The syndrome of apathy, defined as a reduction in goal-directed behavior (GDB), has profound consequences for morbidity and mortality in the patient and for family-caregiver burden. Apathy is one of the primary neuropsychiatric syndromes associated with the disruption of the frontal-striatal system, but the behavioral and biological mechanisms underlying apathy are not well understood. Apathy is especially prevalent in behavioral variant frontotemporal degeneration (bvFTD). In a sample of 20 apathetic adults with bvFTD and 17 normal controls (NC), impairments in three components of GDB--initiation, planning and motivation--were examined using a novel computerized reaction time test. Employing structural neuroimaging techniques, I then examined the neural basis of GDB in these apathetic bvFTD participants. I found evidence that apathy is associated with an impairment in any of the three GDB components. Initiation, planning, and motivation each map onto three distinct brain regions in the frontal lobe that work together in a large-scale neural network. Furthermore, I was able to identify participants with specific subtypes of apathy, depending on the impaired GDB mechanism. I developed and submitted a proposal for continued study of the phenomenon; the proposal was awarded. The long-term potential impact of this beginning program of research is profound for patients with neurodegenerative disease, their caregivers, and families. Current treatment of apathy has been hindered due to poor understanding of the mechanisms underlying this condition. This work will lead to a better understanding of these mechanisms and structures fundamental to the behavior, and, with this knowledge, tailored interventions can be designed and implemented by professional and lay caregivers. Thus, a more precise characterization of apathy will allow providers to implement the most appropriate therapy for a given patient

The Role of the Cerebellum in Schizophrenia: an Update of Clinical, Cognitive, and Functional Evidences

The role of the cerebellum in schizophrenia has been highlighted by Andreasen's hypothesis of “cognitive dysmetria,” which suggests a general dyscoordination of sensorimotor and mental processes. Studies in schizophrenic patients have brought observations supporting a cerebellar impairment: high prevalence of neurological soft signs, dyscoordination, abnormal posture and propioception, impaired eyeblink conditioning, impaired adaptation of the vestibular-ocular reflex or procedural learning tests, and lastly functional neuroimaging studies correlating poor cognitive performances with abnormal cerebellar activations. Despite those compelling evidences, there has been, to our knowledge, no recent review on the clinical, cognitive, and functional literature supporting the role of the cerebellum in schizophrenia. We conducted a Medline research focusing on cerebellar dysfunctions in schizophrenia. Emphasis was given to recent literature (after 1998). The picture arising from this review is heterogeneous. While in some domains, the role of the cerebellum seems clearly defined (ie, neurological soft signs, posture, or equilibrium), in other domains, the cerebellar contribution to schizophrenia seems limited or indirect (ie, cognition) if present at all (ie, affectivity). Functional models of the cerebellum are proposed as a background for interpreting these results

Auditory, phonological and semantic factors in the recovery from Wernicke’s aphasia post stroke: predictive value and implications for rehabilitation

Background: Understanding the factors that influence language recovery in aphasia is important for improving prognosis and treatment. Chronic comprehension impairments Wernicke’s-type aphasia (WA) are associated with impairments in auditory and phonological processing, compounded by semantic and executive difficulties. This study investigated whether the recovery of auditory, phonological, semantic or executive factors underpins the recovery from WA comprehension impairments by charting changes in the neuropsychological profiles from the sub-acute to the chronic phase. Method: This study used a prospective, longitudinal, observational design. Twelve WA participants with superior temporal lobe lesions were recruited before 2 months post stroke onset (MPO). Language comprehension was measured alongside a neuropsychological profile of auditory, phonological and semantic processing alongside phonological short-term memory and nonverbal reasoning at three post stroke time points: 2.5, 5 and 9MPO. Results: Language comprehension displayed a strong and consistent recovery between 2.5 and 9MPO. Improvements were also seen for slow auditory temporal processing, phonological short-term memory, and semantic processing, but not for rapid auditory temporal, spectrotemporal and phonological processing. Despite their lack of improvement, rapid auditory temporal processing at 2.5MPO and phonological processing at 5MPO predicated comprehension outcomes at 9MPO. Conclusions: These results indicate that recovery of language comprehension in WA can be predicted from fixed auditory processing in the subacute stage. This suggests that speech comprehension recovery in WA results from reorganisation of the remaining language comprehension network to enable the residual speech signal to be processed more efficiently, rather than partial recovery of underlying auditory, phonological or semantic processing abilities