Joey A. Contreras

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.

COGNITIVE COMPLAINTS IN OLDER ADULTS AT RISK FOR ALZHEIMER’S DISEASE ARE ASSOCIATED WITH ALTERED RESTING STATE NETWORKS

of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands; 3 Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands; 4 VU University Medical Center, Amsterdam, Netherlands; Neurocampus Amsterdam, VU University Medical Center, Amsterdam, Netherlands; Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands; University College London, London, United Kingdom. Contact e-mail: b.tijms@vumc.nl

Integrated Visualization of Human Brain Connectome Data

Visualization plays a vital role in the analysis of multi-modal 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 anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. 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

Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimers 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.

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.

CSF SPDGFRB, A MEASURE OF VASCULAR DYSFUNCTION, IS RELATED TO DISRUPTED FUNCTIONAL CONNECTIVITY AMONG BRAIN REGIONS ASSOCIATED WITH ALZHEIMER’S DISEASE

Germany; German Center for Neurodegenerative Diseases, Munich, Germany; University Medical Center, Bonn, Germany; Charit e – Universit€atsmedizin Berlin, Berlin, Germany; German Center for Neurodegenerative Diseases, Berlin, Germany; Charit e Universit€atsmedizin Berlin and Berlin Institute of Health, Berlin, Germany; German Center for Neurodegenerative Diseases, Goettingen, Germany; University Medical Center Goettingen, Georg-August-University, Goettingen, Germany; University of T€ubingen, T€ubingen, Germany; German Center for Neurodegenerative Diseases, T€ubingen, Germany; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany; University of Cologne, Cologne, Germany. Contact e-mail: JanOliver.Kuper-Smith@ med.uni-rostock.de

NEUROINFLAMMATION IN ALZHEIMER’S DISEASE: TNF-α IS ASSOCIATED WITH ALTERED FUNCTIONAL CONNECTIVITY AMONG REWARD NETWORKS

(via CSF or Amyloid PET) underwent Tau PET scanning with FPI2620 on a GE PET-MRI scanner: ten older clinically normal (CN) individuals (five AmyloidCN, mean age1⁄472.664.0; and five Amyloid+ CN, mean age1⁄472.266.8), six clinically impaired patients on the AD trajectory (mean age1⁄465.068.2; three Amyloid+ Mild Cognitive Impairment and three Amyloid+ AD dementia), as well as one Amyloidpatient with Dementia with Lewy Bodies (DLB). Standardized uptake value ratios were computed 60-90 minutes post-injection and normalized to the inferior cerebellum. We examined target regions known to show high Tau uptake in AD (entorhinal cortex, hippocampus, amygdala, inferior temporal cortex, precuneus, and lateral parietal cortex). Group differences (AmyloidCN vs. Amyloid+ CN vs. Amyloid+ Impaired) in regional Tau were assessed with Wilcoxon signed-rank tests, whereas associations between continuous CSF measures (Ab42 and pTau) with regional Tau within the CN group were assessed with Spearman’s Rank correlation coefficients. Results: Compared to AmyloidCN, Amyloid+ CN showed greater PI2620 uptake in entorhinal cortex, hippocampus, and amygdala (p-values<0.032). The Amyloid+ Impaired group showed elevated Tau in all regions compared to AmyloidCN (p-values<0.008), as well as elevated Tau in inferior temporal cortex (p1⁄4 0.016), precuneus (p<0.001), and lateral parietal cortex (p<0.001) compared to the Amyloid+ CN group (Figure 1). Within the CN group, continuous levels of CSF Ab42 were negatively associated with elevated Tau PET in entorhinal cortext (p1⁄40.026), hippocampus (p1⁄40.004), and amygdala (p1⁄40.007). CSF pTau was not related to any regional Tau PET value (Figure 2). The AmyloidDLB case did not show evidence of uptake in any Tau PET region. Conclusions:Preliminary results suggest strong differences in cortical uptake of F-PI2620 in Amyloid+ impaired patients compared to older CN. Amyloid related differences among the CN were detected in medial temporal lobe regions. This work suggests promise for F-PI2620 in detecting Tau aggregation throughout the course of AD.

RESTING STATE NETWORK MODULARITY ALONG THE PRODROMAL LATE ONSET ALZHEIMER’S DISEASE CONTINUUM

Alzheimers disease is considered a disconnection syndrome, motivating the use of brain network measures to detect changes in whole-brain resting state functional connectivity (FC). We investigated changes in FC within and among resting state networks (RSN) across four different stages in the Alzheimers disease continuum. FC changes were examined in two independent cohorts of individuals (84 and 58 individuals, respectively) each comprising control, subjective cognitive decline, mild cognitive impairment and Alzheimers dementia groups. For each participant, FC was computed as a matrix of Pearson correlations between pairs of time series from 278 gray matter brain regions. We determined significant differences in FC modular organization with two distinct approaches, network contingency analysis and multiresolution consensus clustering. Network contingency analysis identified RSN sub-blocks that differed significantly across clinical groups. Multiresolution consensus clustering identified differences in the stability of modules across multiple spatial scales. Significant modules were further tested for statistical association with memory and executive function cognitive domain scores. Across both analytic approaches and in both participant cohorts, the findings converged on a pattern of FC that varied systematically with diagnosis within the frontoparietal network (FP) and between the FP network and default mode network (DMN). Disturbances of modular organization were manifest as greater internal coherence of the FP network and stronger coupling between FP and DMN, resulting in less segregation of these two networks. Our findings suggest that the pattern of interactions within and between specific RSNs offers new insight into the functional disruption that occurs across the Alzheimers disease spectrum.

Association of eye disease with increased diffusivity in the sagittal stratum

P3-134 ASSOCIATION OF EYE DISEASE WITH INCREASED DIFFUSIVITY IN THE SAGITTAL STRATUM Shannon L. Risacher, Li Shen, Joaqu ın Go~ni, Joey A. Contreras, John D. West, Sujuan Gao, Paul M. Thompson, Paul S. Aisen, Clifford R. Jack, Jr,, Ronald C. Petersen, Michael W. Weiner, Andrew J. Saykin, Indiana University School of Medicine, Indianapolis, IN, USA; University of Southern California, Los Angeles, CA, USA; University of California, San Diego, La Jolla, CA, USA; Mayo Clinic, Rochester, MN, USA; University of California San Francisco, San Francisco, CA, USA. Contact e-mail: srisache@iupui.edu