Mismatch between clinically defined classification of ALS stage and the burden of cerebral pathology.

This study aimed to investigate the clinical stratification of amyotrophic lateral sclerosis (ALS) patients in relation to in vivo cerebral degeneration. One hundred forty-nine ALS patients and one hundred forty-four healthy controls (HCs) were recruited from the Canadian ALS Neuroimaging Consortium (CALSNIC). Texture analysis was performed on T1-weighted scans to extract the texture feature "autocorrelation" (autoc), an imaging biomarker of cerebral degeneration. Patients were stratified at baseline into early and advanced disease stages based on criteria adapted from ALS clinical trials and the King's College staging system, as well as into slow and fast progressors (disease progression rates, DPR). Patients had increased autoc in the internal capsule. These changes extended beyond the internal capsule in early-stage patients (clinical trial-based criteria), fast progressors, and in advanced-stage patients (King's staging criteria). Longitudinal increases in autoc were observed in the postcentral gyrus, corticospinal tract, posterior cingulate cortex, and putamen; whereas decreases were observed in corpus callosum, caudate, central opercular cortex, and frontotemporal areas. Both longitudinal increases and decreases of autoc were observed in non-overlapping regions within insula and precentral gyrus. Within-criteria comparisons of autoc revealed more pronounced changes at baseline and longitudinally in early- (clinical trial-based criteria) and advanced-stage (King's staging criteria) patients and fast progressors. In summary, comparative patterns of baseline and longitudinal progression in cerebral degeneration are dependent on sub-group selection criteria, with clinical trial-based stratification insufficiently characterizing disease stage based on pathological cerebral burden.

Genetic map of regional sulcal morphology in the human brain from UK biobank data.

Genetic associations with macroscopic brain structure can provide insights into brain function and disease. However, specific associations with measures of local brain folding are largely under-explored. Here, we conducted large-scale genome- and exome-wide associations of regional cortical sulcal measures derived from magnetic resonance imaging scans of 40,169 individuals in UK Biobank. We discovered 388 regional brain folding associations across 77 genetic loci, with genes in associated loci enriched for expression in the cerebral cortex, neuronal development processes, and differential regulation during early brain development. We integrated brain eQTLs to refine genes for various loci, implicated several genes involved in neurodevelopmental disorders, and highlighted global genetic correlations with neuropsychiatric phenotypes. We provide an interactive 3D visualisation of our summary associations, emphasising added resolution of regional analyses. Our results offer new insights into the genetic architecture of brain folding and provide a resource for future studies of sulcal morphology in health and disease.

Cognitive function and neuropathological outcomes: a forward-looking approach.

OBJECTIVE: To evaluate the risk of Alzheimer's disease-related neuropathology burden at autopsy given older adults' current cognitive state. METHOD: Participants included 1,303 individuals who enrolled in the Religious Orders Study (ROS) and 1,789 who enrolled in the Rush Memory and Aging Project (MAP). Cognitive status was evaluated via standardized assessments of global cognition and episodic memory. At the time of analyses, about 50% of participants were deceased with the remaining numbers right censored. Using multi-state Cox proportional hazard models, we compared the cognitive status of all subjects alive at a given age and estimated future risk of dying with different AD-related neuropathologies. Endpoints considered were Braak Stages (0-2, 3-4, 5-6), CERAD (0, 1, 2, 3), and TDP-43 (0, 1, 2, 3) level. RESULTS: For all three pathological groupings (Braak, CERAD, TDP-43), we found that a cognitive test score one standard deviation below average put individuals at up to three times the risk for being diagnosed with late stage AD at autopsy according to pathological designations. The effect remained significant after adjusting for sex, APOE-e4 status, smoking status, education level, and vascular health scores. CONCLUSION: Applying multi-state modeling techniques, we were able to identify those at risk of exhibiting specific levels of neuropathology based on current cognitive test performance. This approach presents new and approachable possibilities in clinical settings for diagnosis and treatment development programs.

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

APOEε4 Genotype and Hypertension Modify 8-year Cortical Thinning: Five Occasion Evidence from the Seattle Longitudinal Study.

We investigated individual differences in longitudinal trajectories of brain aging in cognitively normal healthy adults from the Seattle Longitudinal Study covering 8 years of longitudinal change (across 5 occasions) in cortical thickness in 249 midlife and older adults (52-95 years old). We aimed to understand true brain change; examine the influence of salient risk factors that modify an individual's rate of cortical thinning; and compare cross-sectional age-related differences in cortical thickness to longitudinal within-person cortical thinning. We used Multivariate Multilevel Modeling to simultaneously model dependencies among 5 lobar composites (Frontal, Parietal, Temporal, Occipital, and Cingulate [CING]) and account for the longitudinal nature of the data. Results indicate (1) all 5 lobar composites significantly atrophied across 8 years, showing nonlinear longitudinal rate of cortical thinning decelerated over time, (2) longitudinal thinning was significantly altered by hypertension and Apolipoprotein-E ε4 (APOEε4), varying by location: Frontal and CING thinned more rapidly in APOEε4 carriers. Notably, thinning of parietal and occipital cortex showed synergistic effect of combined risk factors, where individuals who were both APOEε4 carriers and hypertensive had significantly greater 8-year thinning than those with either risk factor alone or neither risk factor, (3) longitudinal thinning was 3 times greater than cross-sectional estimates of age-related differences in thickness in parietal and occipital cortices.

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.

During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced. The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as "OptiC", from a large dataset involving 20 centers, 4 contrasts (T2-weighted n = 287, T1-weighted n = 120, T2∗-weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions. Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a mean square error of 1.02 mm. OptiC achieved superior results compared to a state-of-the-art spinal cord localization technique based on the Hough transform, especially on pathological cases with an averaged mean square error of 1.08 mm vs. 13.16 mm (Wilcoxon signed-rank test p-value < .01). Images containing brain regions were identified with a 99% precision, on which brain and spine regions were separated with a distance error of 9.37 mm compared to ground-truth. Validation results on a challenging dataset suggest that OptiC could reliably be used for subsequent quantitative analyses tasks, opening the door to more robust analysis on pathological cases.

Smoke-Free Home and Vehicle Policies Among Community College Smokers.

BACKGROUND: Personal smoke-free policies (home and vehicle) reduce secondhand smoke exposure, improve health, and increase quitting among smokers. Overall, 83.0% and 78.1% of Americans report smoke-free homes and vehicles, respectively. However, little is known about such policies among 2-year community college (CC) students, who represent a large, diverse population with higher smoking rates and less negative attitudes toward smoking than 4-year college students. METHODS: Prevalence of, and factors associated with, personal smoke-free policies were examined for 2,475 CC smokers enrolled in a national trial of web-assisted tobacco intervention. RESULTS: Few students had smoke-free home policies (20.7%), smoke-free vehicles (17.0%), both smoke-free home and vehicle policy (4.2%), or any policy (home or vehicle; 31.2%). In logistic regression models, having children was associated with a smoke-free home or any policy but not with a smoke-free vehicle, and among participants who had children, only 20% reported a smoke-free home, and only 15% had a smoke-free vehicle. In addition, not living with other smokers, living with parents or roommates/siblings (vs. alone), smoking later than 30 minutes after awakening, believing that smoking affects the health of others, and confidence in quitting were associated with presence of a smoke-free home or any policy; no variables were significantly associated with presence of a smoke-free vehicle. CONCLUSIONS: CC students represent a priority population for intervention regarding smoke-free homes and vehicles. Such intervention can decrease exposure of others, including children, and potentially increase the likelihood of quitting in this high-risk population.

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease.

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of correlation of the BOLD signal in spatially independent regions of the brain defines the functional connectivity of those regions. During a cognitive fMRI task, a psychophysiological interaction (PPI) analysis can be used to examine changes in the functional connectivity during specific contexts defined by the cognitive task. An example of such a task is one that engages the memory system, asking participants to learn pairs of unrelated words (encoding) and recall the second word in a pair when presented with the first word (retrieval). In the present study, we used this type of associative memory task and a generalized PPI (gPPI) analysis to compare changes in hippocampal connectivity in older adults who are carriers of the Alzheimer's disease (AD) genetic risk factor apolipoprotein-E epsilon-4 (APOEε4). Specifically, we show that the functional connectivity of subregions of the hippocampus changes during encoding and retrieval, the two active phases of the associative memory task. Context-dependent changes in functional connectivity of the hippocampus were significantly different in carriers of APOEε4 compared to non-carriers. PPI analyses make it possible to examine changes in functional connectivity, distinct from univariate main effects, and to compare these changes across groups. Thus, a PPI analysis may reveal complex task effects in specific cohorts that traditional univariate methods do not capture. PPI analyses cannot, however, determine directionality or causality between functionally connected regions. Nevertheless, PPI analyses provide powerful means for generating specific hypotheses regarding functional relationships, which can be tested using causal models. As the brain is increasingly described in terms of connectivity and networks, PPI is an important method for analyzing fMRI task data that is in line with the current conception of the human brain.

Inter-Rater Reliability of Preprocessing EEG Data: Impact of Subjective Artifact Removal on Associative Memory Task ERP Results.

The processing of EEG data routinely involves subjective removal of artifacts during a preprocessing stage. Preprocessing inter-rater reliability (IRR) and how differences in preprocessing may affect outcomes of primary event-related potential (ERP) analyses has not been previously assessed. Three raters independently preprocessed EEG data of 16 cognitively healthy adult participants (ages 18-39 years) who performed a memory task. Using intraclass correlations (ICCs), IRR was assessed for Early-frontal, Late-frontal, and Parietal Old/new memory effects contrasts across eight regions of interest (ROIs). IRR was good to excellent for all ROIs; 22 of 26 ICCs were above 0.80. Raters were highly consistent in preprocessing across ROIs, although the frontal pole ROI (ICC range 0.60-0.90) showed less consistency. Old/new parietal effects had highest ICCs with the lowest variability. Rater preprocessing differences did not alter primary ERP results. IRR for EEG preprocessing was good to excellent, and subjective rater-removal of EEG artifacts did not alter primary memory-task ERP results. Findings provide preliminary support for robustness of cognitive/memory task-related ERP results against significant inter-rater preprocessing variability and suggest reliability of EEG to assess cognitive-neurophysiological processes multiple preprocessors are involved.

Memory self-awareness in the preclinical and prodromal stages of Alzheimer's disease.

While loss of insight of cognitive deficits, anosognosia, is a common symptom in Alzheimer's disease dementia, there is a lack of consensus regarding the presence of altered awareness of memory function in the preclinical and prodromal stages of the disease. Paradoxically, very early in the Alzheimer's disease process, individuals may experience heightened awareness of memory changes before any objective cognitive deficits can be detected, here referred to as hypernosognosia. In contrast, awareness of memory dysfunction shown by individuals with mild cognitive impairment (MCI) is very variable, ranging from marked concern to severe lack of insight. This study aims at improving our mechanistic understanding of how alterations in memory self-awareness are related to pathological changes in clinically normal (CN) adults and MCI patients. 297 CN and MCI patients underwent PiB-PET (Positron Emission Tomography using Pittsburgh Compound B) in vivo amyloid imaging. Amyloid burden was estimated from Alzheimer's disease vulnerable regions, including the frontal, lateral parietal and lateral temporal, and retrosplenial cortex. Memory self-awareness was assessed using discrepancy scores between subjective and objective measures of memory function. A set of univariate analysis of variance were performed to assess the relationship between self-awareness of memory and amyloid pathology. Whereas CN individuals harboring amyloid pathology demonstrated hypernosognosia, MCI patients with increased amyloid pathology demonstrated anosognosia. In contrast, MCI patients with low amounts of amyloid were observed to have normal insight into their memory functions. Altered self-awareness of memory tracks with amyloid pathology. The findings of variability of awareness may have important implications for the reliability of self-report of dysfunction across the spectrum of preclinical and prodromal Alzheimer's disease.

Harvard Aging Brain Study: Dataset and accessibility.

The Harvard Aging Brain Study is sharing its data with the global research community. The longitudinal dataset consists of a 284-subject cohort with the following modalities acquired: demographics, clinical assessment, comprehensive neuropsychological testing, clinical biomarkers, and neuroimaging. To promote more extensive analyses, imaging data was designed to be compatible with other publicly available datasets. A cloud-based system enables access to interested researchers with blinded data available contingent upon completion of a data usage agreement and administrative approval. Data collection is ongoing and currently in its fifth year.

Asymptomatic Alzheimer disease: Defining resilience.

OBJECTIVE: To define robust resilience metrics by leveraging CSF biomarkers of Alzheimer disease (AD) pathology within a latent variable framework and to demonstrate the ability of such metrics to predict slower rates of cognitive decline and protection against diagnostic conversion. METHODS: Participants with normal cognition (n = 297) and mild cognitive impairment (n = 432) were drawn from the Alzheimer's Disease Neuroimaging Initiative. Resilience metrics were defined at baseline by examining the residuals when regressing brain aging outcomes (hippocampal volume and cognition) on CSF biomarkers. A positive residual reflected better outcomes than expected for a given level of pathology (high resilience). Residuals were integrated into a latent variable model of resilience and validated by testing their ability to independently predict diagnostic conversion, cognitive decline, and the rate of ventricular dilation. RESULTS: Latent variables of resilience predicted a decreased risk of conversion (hazard ratio < 0.54, p < 0.0001), slower cognitive decline (ß > 0.02, p < 0.001), and slower rates of ventricular dilation (ß < -4.7, p < 2 × 10-15). These results were significant even when analyses were restricted to clinically normal individuals. Furthermore, resilience metrics interacted with biomarker status such that biomarker-positive individuals with low resilience showed the greatest risk of subsequent decline. CONCLUSIONS: Robust phenotypes of resilience calculated by leveraging AD biomarkers and baseline brain aging outcomes provide insight into which individuals are at greatest risk of short-term decline. Such comprehensive definitions of resilience are needed to further our understanding of the mechanisms that protect individuals from the clinical manifestation of AD dementia, especially among biomarker-positive individuals.

Modulation of glucose metabolism and metabolic connectivity by ß-amyloid.

Glucose hypometabolism in the pre-clinical stage of Alzheimer's disease (AD) has been primarily associated with the APOE ɛ4 genotype, rather than fibrillar ß-amyloid. In contrast, aberrant patterns of metabolic connectivity are more strongly related to ß-amyloid burden than APOE ɛ4 status. A major limitation of previous studies has been the dichotomous classification of subjects as amyloid-positive or amyloid-negative. Dichotomous treatment of a continuous variable, such as ß-amyloid, potentially obscures the true relationship with metabolism and reduces the power to detect significant changes in connectivity. In the present work, we assessed alterations of glucose metabolism and metabolic connectivity as continuous function of ß-amyloid burden using positron emission tomography scans from the Alzheimer's Disease Neuroimaging Initiative study. Modeling ß-amyloid as a continuous variable resulted in better model fits and improved power compared to the dichotomous model. Using this continuous model, we found that both APOE ɛ4 genotype and ß-amyloid burden are strongly associated with glucose hypometabolism at early stages of Alzheimer's disease. We also determined that the cumulative effects of ß-amyloid deposition result in a particular pattern of altered metabolic connectivity, which is characterized by global, synchronized hypometabolism at early stages of the disease process, followed by regionally heterogeneous, progressive hypometabolism.

Brainhack: a collaborative workshop for the open neuroscience community.

Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.

Prior hospital admission predicts thirty-day hospital readmission for heart failure patients.

BACKGROUND: Hospital readmission is a significant health burden. More than 20% of heart failure (HF) patients are readmitted within 30 days of discharge leading to billions of dollars in health care expenditures. However, the role of prior hospital admissions to predict 30-day readmission for HF patients is not fully understood. METHODS: We retrospectively analyzed HF hospitalization data for 4 years at a single medical center. Association between prior admission and 30-day readmission after HF hospitalization was assessed using a multivariate logistic regression model. RESULTS: A total of 1,999 patients with index HF hospitalizations were identified, and 366 of them (18%) were readmitted within 30 days. The rate of readmission was 14%, 20%, and 33% in patients with 0, 1, ≥ 2 prior admissions. Patients with one prior admission had a 50% higher risk (confidence interval [CI] 1.10-2.05, p = 0.011) for readmission, while those with ≥ 2 prior admissions had a more than 3-fold increase in readmission (CI 2.27-4.09, p < 0.001), after adjustments for relevant clinical covariates. Prior hospital admission provided incremen-tal value in predicting readmissions, shown by the significant improvement in the readmission predictive model (C-statistics increased from 0.57 to 0.63). However, neither the length of stay nor recency of prior admission was a significant factor in predicting readmissions. CONCLUSIONS: Hospital admission prior to an index HF hospitalization is associated with a significantly increased risk for 30-day hospital readmission and could be used to identify patients at high-risk for readmission and potentially target interventions to reduce the risk of readmission for these patients.

Recognition of faces and names: multimodal physiological correlates of memory and executive function.

We sought to characterize electrophysiological, eye-tracking and behavioral correlates of face-name recognition memory in healthy younger adults using high-density electroencephalography (EEG), infrared eye-tracking (ET), and neuropsychological measures. Twenty-one participants first studied 40 face-name (FN) pairs; 20 were presented four times (4R) and 20 were shown once (1R). Recognition memory was assessed by asking participants to make old/new judgments for 80 FN pairs, of which half were previously studied items and half were novel FN pairs (N). Simultaneous EEG and ET recording were collected during recognition trials. Comparisons of event-related potentials (ERPs) for correctly identified FN pairs were compared across the three item types revealing classic ERP old/new effects including 1) relative positivity (1R > N) bi-frontally from 300 to 500 ms, reflecting enhanced familiarity, 2) relative positivity (4R > 1R and 4R > N) in parietal areas from 500 to 800 ms, reflecting enhanced recollection, and 3) late frontal effects (1R > N) from 1000 to 1800 ms in right frontal areas, reflecting post-retrieval monitoring. ET analysis also revealed significant differences in eye movements across conditions. Exploration of cross-modality relationships suggested associations between memory and executive function measures and the three ERP effects. Executive function measures were associated with several indicators of saccadic eye movements and fixations, which were also associated with all three ERP effects. This novel characterization of face-name recognition memory performance using simultaneous EEG and ET reproduced classic ERP and ET effects, supports the construct validity of the multimodal FN paradigm, and holds promise as an integrative tool to probe brain networks supporting memory and executive functioning.

Shaped by the Past: The Default Mode Network Supports Cognition that Is Independent of Immediate Perceptual Input.

Although many different accounts of the functions of the default mode network (DMN) have been proposed, few can adequately account for the spectrum of different cognitive functions that utilize this network. The current study used functional magnetic resonance imaging (fMRI) to explore the hypothesis that the role of the DMN in higher order cognition is to allow cognition to be shaped by information from stored representations rather than information in the immediate environment. Using a novel task paradigm, we observed increased BOLD activity in regions of the medial prefrontal cortex and posterior cingulate cortex when individuals made decisions on the location of shapes from the prior trial and decreased BOLD activity when individuals made decisions on the location of shapes on the current trial. These data are inconsistent with views of the DMN as a task-negative system or one that is sensitive only to stimuli with strong personal or emotional ties. Instead the involvement of the DMN when people make decisions about where a shape was, rather than where it is now, supports the hypothesis that the core hubs of the DMN allow cognition to be guided by information other than the immediate perceptual input. We propose that a variety of different forms of higher order thought (such as imagining the future or considering the perspective of another person) engage the DMN because these more complex introspective forms of higher order thought all depend on the capacity for cognition to be shaped by representations that are not present in the external environment.

Template based rotation: a method for functional connectivity analysis with a priori templates.

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,(1) a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium.

Flexible modulation of network connectivity related to cognition in Alzheimer's disease.

Functional neuroimaging tools, such as fMRI methods, may elucidate the neural correlates of clinical, behavioral, and cognitive performance. Most functional imaging studies focus on regional task-related activity or resting state connectivity rather than how changes in functional connectivity across conditions and tasks are related to cognitive and behavioral performance. To investigate the promise of characterizing context-dependent connectivity-behavior relationships, this study applies the method of generalized psychophysiological interactions (gPPI) to assess the patterns of associative-memory-related fMRI hippocampal functional connectivity in Alzheimer's disease (AD) associated with performance on memory and other cognitively demanding neuropsychological tests and clinical measures. Twenty-four subjects with mild AD dementia (ages 54-82, nine females) participated in a face-name paired-associate encoding memory study. Generalized PPI analysis was used to estimate the connectivity between the hippocampus and the whole brain during encoding. The difference in hippocampal-whole brain connectivity between encoding novel and encoding repeated face-name pairs was used in multiple-regression analyses as an independent predictor for 10 behavioral, neuropsychological and clinical tests. The analysis revealed connectivity-behavior relationships that were distributed, dynamically overlapping, and task-specific within and across intrinsic networks; hippocampal-whole brain connectivity-behavior relationships were not isolated to single networks, but spanned multiple brain networks. Importantly, these spatially distributed performance patterns were unique for each measure. In general, out-of-network behavioral associations with encoding novel greater than repeated face-name pairs hippocampal-connectivity were observed in the default-mode network, while correlations with encoding repeated greater than novel face-name pairs hippocampal-connectivity were observed in the executive control network (p<0.05, cluster corrected). Psychophysiological interactions revealed significantly more extensive and robust associations between paired-associate encoding task-dependent hippocampal-whole brain connectivity and performance on memory and behavioral/clinical measures than previously revealed by standard activity-behavior analysis. Compared to resting state and task-activation methods, gPPI analyses may be more sensitive to reveal additional complementary information regarding subtle within- and between-network relations. The patterns of robust correlations between hippocampal-whole brain connectivity and behavioral measures identified here suggest that there are 'coordinated states' in the brain; that the dynamic range of these states is related to behavior and cognition; and that these states can be observed and quantified, even in individuals with mild AD.

Amyloid deposition is linked to aberrant entorhinal activity among cognitively normal older adults.

Normal aging is often difficult to distinguish from the earliest stages of Alzheimer's disease. Years before clinical memory deficits manifest, amyloid-ß deposits in the cortex in many older individuals. Neuroimaging studies indicate that a set of densely connected neocortical regions, referred to as the default network, is especially vulnerable to amyloid-ß deposition. Yet, the impact of amyloid-ß on age-related changes within the medial temporal lobe (MTL) memory system is less clear. Here we demonstrate that cognitively normal older humans, compared with young adults, show reduced ability to modulate hippocampal activations and entorhinal deactivations during an episodic memory task. Among older adults, amyloid-ß deposition was associated with failure to modulate activity in entorhinal cortex, but not hippocampus. Furthermore, we show that entorhinal regions demonstrating amyloid-ß-related dysfunction are directly connected to the neocortical regions of the default network. Together these findings link neocortical amyloid-ß deposition to neuronal dysfunction specifically in entorhinal cortex, while aging is associated with more widespread functional changes across the MTL.