The Structural and Functional Connectome and Prediction of Risk for Cognitive Impairment in Older Adults

The human connectome refers to a comprehensive description of the brain's structural and functional connections in terms of brain networks. As the field of brain connectomics has developed, data acquisition, subsequent processing and modeling, and ultimately the representation of the connectome have become better defined and integrated with network science approaches. In this way, the human connectome has provided a way to elucidate key features of not only the healthy brain but also diseased brains. The field has quickly evolved, offering insights into network disruptions that are characteristic for specific neurodegenerative disorders. In this paper, we provide a brief review of the field of brain connectomics, as well as a more in-depth survey of recent studies that have provided new insights into brain network pathologies, including those found in Alzheimer's disease (AD), patients with mild cognitive impairment (MCI), and finally in people classified as being "at risk". Until the emergence of brain connectomics, most previous studies had assessed neurodegenerative diseases mainly by focusing on specific and dispersed locales in the brain. Connectomics-based approaches allow us to model the brain as a network, which allows for inferences about how dynamic changes in brain function would be affected in relation to structural changes. In fact, looking at diseases using network theory gives rise to new hypotheses on mechanisms of pathophysiology and clinical symptoms. Finally, we discuss the future of this field and how understanding both the functional and structural connectome can aid in gaining sharper insight into changes in biological brain networks associated with cognitive impairment and dementia

Brain explorer for connectomic analysis

Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases

Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting-state networks

INTRODUCTION: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. METHODS: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). RESULTS: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. DISCUSSION: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization

Enhanced inhibitory avoidance learning prevents the long-term memory-impairing effects of cycloheximide, a protein synthesis inhibitor

Interference with activity of numerous cerebral structures produces memory deficiencies; in many instances, however, when animals are over-trained such interference becomes innocuous. Systemic administration of protein synthesis inhibitors impairs long-term retention; this effect has been interpreted to mean that protein synthesis is required for memory consolidation, though little is known about the effect of protein synthesis inhibitors on memory of enhanced learning in the rat. To further analyze the protective effect of enhanced learning against amnesic treatments, groups of Wistar rats were trained in a one-trial step-through inhibitory avoidance task, using different intensities of foot-shock during training. Cycloheximide (CXM; 2.8 mg/kg), an inhibitor of protein synthesis, was injected either 30 min before training or immediately after training. Twenty-four hours after training retention latencies were recorded. Our data showed that both pre- and post-training administration of CXM produced amnesia in those groups that had been trained with relatively low foot-shock intensities, but no impairment in retention was observed when relatively high intensities of foot-shock were administered. These and similar results lead us to conclude that protein synthesis inhibitors may interfere with memory consolidation, but their effect disappears when animals are submitted to an enhanced learning experience, calling into question the idea that protein synthesis is required for memory consolidation

Brain explorer for connectomic analysis

Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases

Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting‐state networks

Introduction: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. Methods: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). Results: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. Discussion: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization

Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting-state networks

Introduction: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. Methods: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). Results: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. Discussion: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization

Characterizing neurodegeneration in the human connectome: a network science study of hereditary diffuse leukoencephalopathy with spheroids

poster abstractAbstract The effect of white matter neurodegeneration on the human connectome and its functional implications is an important topic with clinical applicability of advanced brain network analysis. The aim of this study was to evaluate integration and segregation changes in structural connectivity (SC) that arise as consequence of white matter lesions in hereditary diffuse leukoencephalopathy with spheroids (HDLS). Also, we assessed the relationship between HDLS induced structural changes and changes in restingstate functional connectivity (rsFC). HDLS is a rare autosomal dominant neurodegenerative disorder caused by mutations in the CSF1R gene. HDLS is characterized by severe white matter damage leading to prominent subcortical lesions detectable by structural MRI. Spheroids, an important feature of HDLS, are axonal swellings indicating damage. HDLS causes progressive motor and cognitive decline. The clinical symptoms of HDLS are often mistaken for other diseases such as Alzheimer’s disease, frontotemporal dementia, atypical Parkinsonism or multiple sclerosis. Our study is focused on the follow-up of two siblings, one being a healthy control (HC) and the other one being an HDLS patient. In this study, deterministic fiber-tractography of diffusion MRI with multi-tensor modeling was used in order to obtain reliable and reproducible SC matrices. Integration changes were measured by means of SC shortest-paths (including distance and number of edges), whereas segregation and community organization were measured by means of a multiplex modularity analysis on the SC matrices. Additionally, rsFC was modeled using state of the art preprocessing methods including motion regressors and scrubbing. This allowed us to characterize functional changes associated to the disease. Major integration disruption involved superior frontal (L,R), caudal middle frontal (R), precentral (L,R), inferior parietal (R), insula (R) and paracentral (L) regions. Major segregation changes were characterized by the disruption of a large bilateral module that was observed in the HC that includes the frontal pole (L,R), medial orbitofrontal (L,R), rostral middle frontal (L), superior frontal (L,R), precentral (L,R), paracentral (L,R), rostral anterior cingulate (L,R), caudal anterior cingulate (L,R), posterior cingulate (L,R), postcentral (L), precuneus (L,R), lateral orbitofrontal (R) and parsorbitalis (R). The combination of tractography and network analysis permitted the detection and characterization of profound cortical to cortical changes in integration and segregation associated with HDLS white matter lesions and its relationship with rsFC. Our preliminary findings suggest that advanced network analytic approaches show promising sensitivity to known white matter pathology and progression. Further Indiana Alzheimer Disease Center Symposium. March 6, 2015. research is needed to address the specificity of network profiles for differentiation among white matter pathologies and diseases