Personalized brain models identify neurotransmitter receptor changes in Alzheimer's disease.

Alzheimer's disease involves many neurobiological alterations from molecular to macroscopic spatial scales, but we currently lack integrative, mechanistic brain models characterizing how factors across different biological scales interact to cause clinical deterioration in a way that is subject-specific or personalized. As important signalling molecules and mediators of many neurobiological interactions, neurotransmitter receptors are promising candidates for identifying molecular mechanisms and drug targets in Alzheimer's disease. We present a neurotransmitter receptor-enriched multifactorial brain model, which integrates spatial distribution patterns of 15 neurotransmitter receptors from post-mortem autoradiography with multiple in vivo neuroimaging modalities (tau, amyloid-ß and glucose PET, and structural, functional and arterial spin labelling MRI) in a personalized, generative, whole-brain formulation. In a heterogeneous aged population (n = 423, ADNI data), models with personalized receptor-neuroimaging interactions showed a significant improvement over neuroimaging-only models, explaining about 70% (±20%) of the variance in longitudinal changes to the six neuroimaging modalities. In Alzheimer's disease patients (n = 25, ADNI data), receptor-imaging interactions explained up to 39.7% (P < 0.003, family-wise error-rate-corrected) of inter-individual variability in cognitive deterioration, via an axis primarily affecting executive function. Notably, based on their contribution to the clinical severity in Alzheimer's disease, we found significant functional alterations to glutamatergic interactions affecting tau accumulation and neural activity dysfunction and GABAergic interactions concurrently affecting neural activity dysfunction, amyloid and tau distributions, as well as significant cholinergic receptor effects on tau accumulation. Overall, GABAergic alterations had the largest effect on cognitive impairment (particularly executive function) in our Alzheimer's disease cohort (n = 25). Furthermore, we demonstrate the clinical applicability of this approach by characterizing subjects based on individualized 'fingerprints' of receptor alterations. This study introduces the first robust, data-driven framework for integrating several neurotransmitter receptors, multimodal neuroimaging and clinical data in a flexible and interpretable brain model. It enables further understanding of the mechanistic neuropathological basis of neurodegenerative progression and heterogeneity, and constitutes a promising step towards implementing personalized, neurotransmitter-based treatments.

High prognostic value of measurable residual disease detection by flow cytometry in chronic lymphocytic leukemia patients treated with front-line fludarabine, cyclophosphamide, and rituximab, followed by three years of rituximab maintenance.

It has been postulated that monitoring measurable residual disease (MRD) could be used as a surrogate marker of progression-free survival (PFS) in chronic lymphocytic leukemia (CLL) patients after treatment with immunochemotherapy regimens. In this study, we analyzed the outcome of 84 patients at 3 years of follow-up after first-line treatment with fludarabine, cyclophosphamide and rituximab (FCR) induction followed by 36 months of rituximab maintenance thearpy. MRD was assessed by a quantitative four-color flow cytometry panel with a sensitivity level of 10-4 Eighty out of 84 evaluable patients (95.2%) achieved at least a partial response or better at the end of induction. After clinical evaluation, 74 patients went into rituximab maintenance and the primary endpoint was assessed in the final analysis at 3 years of follow-up. Bone marrow (BM) MRD analysis was performed after the last planned induction course and every 6 months in cases with detectable residual disease during the 36 months of maintenance therapy. Thirty-seven patients (44%) did not have detectable residual disease in the BM prior to maintenance therapy. Interestingly, 29 patients with detectable residual disease in the BM after induction no longer had detectable disease in the BM following maintenance therapy. After a median followup of 6.30 years, the median overall survival (OS) and PFS had not been reached in patients with either undetectable or detectable residual disease in the BM, who had achieved a complete response at the time of starting maintenance therapy. Interestingly, univariate analysis showed that after rituximab maintenance OS was not affected by IGHV status (mutated vs unmutated OS: 85.7% alive at 7.2 years vs 79.6% alive at 7.3 years, respectively). As per protocol, 15 patients (17.8%), who achieved a complete response and undetectable peripheral blood and BM residual disease after four courses of induction, were allowed to stop fludarabine and cyclophosphamide and complete two additional courses of rituximab and continue with maintenance therapy for 18 cycles. Surprisingly, the outcome in this population was similar to that observed in patients who received the full six cycles of the induction regimen. These data show that, compared to historic controls, patients treated with FCR followed by rituximab maintenance have high-quality responses with fewer relapses and improved OS. The tolerability of this regime is favorable. Furthermore, attaining an early undetectable residual disease status could shorten the duration of chemoimmunotherapy, reducing toxicities and preventing long-term side effects. The analysis of BM MRD after fludarabine-based induction could be a powerful predictor of post-maintenance outcomes in patients with CLL undergoing rituximab maintenance and could be a valuable tool to identify patients at high risk of relapse, influencing further treatment strategies. This trial is registered with EudraCT n. 2007-002733-36 and ClinicalTrials.gov Identifier: NCT00545714.

Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to neurodegeneration.

Personalized Medicine (PM) seeks to assist the patients according to their specific treatment needs and potential intervention responses. However, in the neurological context, this approach is limited by crucial methodological challenges, such as the requirement for an understanding of the causal disease mechanisms and the inability to predict the brain's response to therapeutic interventions. Here, we introduce and validate the concept of the personalized Therapeutic Intervention Fingerprint (pTIF), which predicts the effectiveness of potential interventions for controlling a patient's disease evolution. Each subject's pTIF can be inferred from multimodal longitudinal imaging (e.g. amyloid-ß, metabolic and tau PET; vascular, functional and structural MRI). We studied an aging population (N = 331) comprising cognitively normal and neurodegenerative patients, longitudinally scanned using six different neuroimaging modalities. We found that the resulting pTIF vastly outperforms cognitive and clinical evaluations on predicting individual variability in gene expression (GE) profiles. Furthermore, after regrouping the patients according to their predicted primary single-target interventions, we observed that these pTIF-based subgroups present distinctively altered molecular pathway signatures, supporting the across-population identification of dissimilar pathological stages, in active correspondence with different therapeutic needs. The results further evidence the imprecision of using broad clinical categories for understanding individual molecular alterations and selecting appropriate therapeutic needs. To our knowledge, this is the first study highlighting the direct link between multifactorial brain dynamics, predicted treatment responses, and molecular alterations at the patient level. Inspired by the principles of PM, the proposed pTIF framework is a promising step towards biomarker-driven assisted therapeutic interventions, with additional important implications for selective enrollment of patients in clinical trials.

Cortical Thickness Abnormalities in Autism Spectrum Disorders Through Late Childhood, Adolescence, and Adulthood: A Large-Scale MRI Study.

Neuroimaging studies in autism spectrum disorders (ASDs) have provided inconsistent evidence of cortical abnormality. This is probably due to the small sample sizes used in most studies, and important differences in sample characteristics, particularly age, as well as to the heterogeneity of the disorder. To address these issues, we assessed abnormalities in ASD within the Autism Brain Imaging Data Exchange data set, which comprises data from approximately 1100 individuals (~6-55 years). A subset of these data that met stringent quality control and inclusion criteria (560 male subjects; 266 ASD; age = 6-35 years) were used to compute age-specific differences in cortical thickness in ASD and the relationship of any such differences to symptom severity of ASD. Our results show widespread increased cortical thickness in ASD, primarily left lateralized, from 6 years onwards, with differences diminishing during adulthood. The severity of symptoms related to social affect and communication correlated with these cortical abnormalities. These results are consistent with the conjecture that developmental patterns of cortical thickness abnormalities reflect delayed cortical maturation and highlight the dynamic nature of morphological abnormalities in ASD.

Multifactorial causal model of brain (dis)organization and therapeutic intervention: Application to Alzheimer's disease.

Generative models focused on multifactorial causal mechanisms in brain disorders are scarce and generally based on limited data. Despite the biological importance of the multiple interacting processes, their effects remain poorly characterized from an integrative analytic perspective. Here, we propose a spatiotemporal multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention that accounts for local causal interactions, effects propagation via physical brain networks, cognitive alterations, and identification of optimum therapeutic interventions. In this article, we focus on describing the model and applying it at the population-based level for studying late onset Alzheimer's disease (LOAD). By interrelating six different neuroimaging modalities and cognitive measurements, this model accurately predicts spatiotemporal alterations in brain amyloid-ß (Aß) burden, glucose metabolism, vascular flow, resting state functional activity, structural properties, and cognitive integrity. The results suggest that a vascular dysregulation may be the most-likely initial pathologic event leading to LOAD. Nevertheless, they also suggest that LOAD it is not caused by a unique dominant biological factor (e.g. vascular or Aß) but by the complex interplay among multiple relevant direct interactions. Furthermore, using theoretical control analysis of the identified population-based multifactorial causal network, we show the crucial advantage of using combinatorial over single-target treatments, explain why one-target Aß based therapies might fail to improve clinical outcomes, and propose an efficiency ranking of possible LOAD interventions. Although still requiring further validation at the individual level, this work presents the first analytic framework for dynamic multifactorial brain (dis)organization that may explain both the pathologic evolution of progressive neurological disorders and operationalize the influence of multiple interventional strategies.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry.

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

Posterior dopamine D2/3 receptors and brain network functional connectivity.

Recent studies suggest that dopaminergic tone influences resting state activity in multiple brain networks. Although dopamine receptors and transporters have been identified in the posteromedial and parietal cortices, which are linked to functional networks such as the default mode network (DMN), the relationship between dopamine receptor distribution in these posterior regions and resting-state connectivity has yet to be explored. Here, we used a multi-modal neuroimaging strategy, combining resting-state functional magnetic resonance imaging (rsfMRI) and [18 F]-fallypride high-resolution positron emission tomography (PET), to examine the association between within-network functional connectivity and the dopamine D2/3 receptor distribution in the posterior portion of the brain in 13 healthy adults. Our results indicate that the posterior distribution of D2/3 receptors coincides primarily with the posterior portion of the DMN. Furthermore, in the posterior portion of the brain, the level of [18 F]-fallypride binding in the posteromedial cortex correlated positively with the functional connectivity strength of the DMN and sensorimotor network, and negatively with the functional connectivity strength of the dorsal attention network, the salience network, and a network that included the anterior part of the temporo-parietal junction. On the basis of these findings, we propose that posterior brain dopamine influences the configuration of the posterior DMN and several other functional brain networks. The posterior distribution of D2/3 receptors binding (hot colour spectrum) coincides with the functional connectivity of the posterior portion of the default mode network (green colour spectrum). The mean BPND in a posteromedial cortex and the mean ICA-Z score in the precuneus showed significant positive correlation.

Impact of the resolution of brain parcels on connectome-wide association studies in fMRI.

A recent trend in functional magnetic resonance imaging is to test for association of clinical disorders with every possible connection between selected brain parcels. We investigated the impact of the resolution of functional brain parcels, ranging from large-scale networks to local regions, on a mass univariate general linear model (GLM) of connectomes. For each resolution taken independently, the Benjamini-Hochberg procedure controlled the false-discovery rate (FDR) at nominal level on realistic simulations. However, the FDR for tests pooled across all resolutions could be inflated compared to the FDR within resolution. This inflation was severe in the presence of no or weak effects, but became negligible for strong effects. We thus developed an omnibus test to establish the overall presence of true discoveries across all resolutions. Although not a guarantee to control the FDR across resolutions, the omnibus test may be used for descriptive analysis of the impact of resolution on a GLM analysis, in complement to a primary analysis at a predefined single resolution. On three real datasets with significant omnibus test (schizophrenia, congenital blindness, motor practice), markedly higher rate of discovery were obtained at low resolutions, below 50, in line with simulations showing increase in sensitivity at such resolutions. This increase in discovery rate came at the cost of a lower ability to localize effects, as low resolution parcels merged many different brain regions together. However, with 30 or more parcels, the statistical effect maps were biologically plausible and very consistent across resolutions. These results show that resolution is a key parameter for GLM-connectome analysis with FDR control, and that a functional brain parcellation with 30 to 50 parcels may lead to an accurate summary of full connectome effects with good sensitivity in many situations.

Rituximab maintenance after first-line therapy with rituximab, fludarabine, cyclophosphamide, and mitoxantrone (R-FCM) for chronic lymphocytic leukemia.

The effectiveness of rituximab maintenance therapy in the treatment of chronic lymphocytic leukemia has been investigated in a phase 2 clinical trial that included an initial treatment with rituximab 500 mg/m2 on day 1 (375 mg/m2 the first cycle), fludarabine 25 mg/m2 on days 1 to 3, cyclophosphamide 200 mg/m2 on days 1 to 3, and mitoxantrone 6 mg/m2 on day 1 (R-FCM), for 6 cycles, followed by a maintenance phase with rituximab 375 mg/m2 every 3 months for 2 years. Sixty-seven patients having achieved complete response (CR) or partial response (PR) with R-FCM were given maintenance therapy. At the end of maintenance, 40.6% of patients were in CR with negative minimal residual disease (MRD), 40.6% were in CR MRD-positive, 4.8% remained in PR, and 14% were considered failures. Six of 29 patients (21%) who were in CR MRD-positive or in PR after R-FCM improved their response upon rituximab maintenance. The 4-year progression-free survival (PFS) and overall survival rates were 74.8% and 93.7%, respectively. MRD status after R-FCM induction was the strongest predictor of PFS. Maintenance with rituximab after R-FCM improved the quality of the response, particularly in patients MRD-positive after initial treatment, and obtained a prolonged PFS. This trial was registered at www.clinicaltrialsregister.eu as identifier #2005-001569-33.

Extra-nodal B-cell non-Hodgkin's lymphomas of the head and neck: a study of 68 cases.

OBJECTIVES: The objectives of this study are to describe the distinctive characteristics of extra-nodal B-cell non-Hodgkin's lymphomas (BNHLs) located in the head and neck in a series of patients, to discuss patient survival, and to compare the oral versus the non-oral locations of the extra-nodal BNHLs of the head and neck. MATERIAL AND METHODS: We studied 68 patients with BNHL of the head and neck. We analyzed the clinical and survival characteristics. Additionally, we performed Kaplan-Meier and Cox regression analyses to determine the influence of the different factors on survival. RESULTS: This study included 68 non-nodal lymphomas; 30 lymphomas (31.9%) were located intraorally, with the gingiva as the most frequent location. The oral lymphomas in stages 1 and 2 showed a prevalence of 60% (18/30). The Kaplan-Meier analysis showed that the stage of disease and the oral versus non-oral extranodal lymphomas were significant prognostic factors (p<0.05). However, the multivariate Cox analysis indicated that only complete remission and oral versus non-oral location were significant prognostic factors (p<0.01). CONCLUSIONS: The gingiva was the most common location of the intraoral lymphomas. Complete remission and non-oral location were the only significant survival factors in the multivariate Cox regression analysis.

Reconstruction of rat retinal progenitor cell lineages in vitro reveals a surprising degree of stochasticity in cell fate decisions.

In vivo cell lineage-tracing studies in the vertebrate retina have revealed that the sizes and cellular compositions of retinal clones are highly variable. It has been challenging to ascertain whether this variability reflects distinct but reproducible lineages among many different retinal progenitor cells (RPCs) or is the product of stochastic fate decisions operating within a population of more equivalent RPCs. To begin to distinguish these possibilities, we developed a method for long-term videomicroscopy to follow the lineages of rat perinatal RPCs cultured at clonal density. In such cultures, cell-cell interactions between two different clones are eliminated and the extracellular environment is kept constant, allowing us to study the cell-intrinsic potential of a given RPC. Quantitative analysis of the reconstructed lineages showed that the mode of division of RPCs is strikingly consistent with a simple stochastic pattern of behavior in which the decision to multiply or differentiate is set by fixed probabilities. The variability seen in the composition and order of cell type genesis within clones is well described by assuming that each of the four different retinal cell types generated at this stage is chosen stochastically by differentiating neurons, with relative probabilities of each type set by their abundance in the mature retina. Although a few of the many possible combinations of cell types within clones occur at frequencies that are incompatible with a fully stochastic model, our results support the notion that stochasticity has a major role during retinal development and therefore possibly in other parts of the central nervous system.

Developmental changes in organization of structural brain networks.

Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics.

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

In-vivo neuronal dysfunction by Aß and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer's disease.

The molecular mechanisms underlying neuronal dysfunction in Alzheimer's disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aß) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aß- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aß and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant's real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aß-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aß and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aß and tau's synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aß-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.

Personalized whole-brain neural mass models reveal combined Aß and tau hyperexcitable influences in Alzheimer's disease.

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-ß (Aß) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aß and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aß-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

Early cortical thickness changes predict ß-amyloid deposition in a mouse model of Alzheimer's disease.

Magnetic resonance imaging (MRI) studies have identified aberrant cortical structure in Alzheimer's disease (AD). The association between MRI-derived cortical morphometry measures and ß-amyloid, however, remains poorly understood. In this study, we explored the potential relationship between early alterations in cortical thickness and later stage ß-amyloid deposition, using a novel approach, in a transgenic AD mouse model. We acquired longitudinal anatomical MRI scans from mutant amyloid precursor protein (APP) transgenic mice and age-matched wild-type mice at 1 and 3.5months-of-age, and employed fully-automated image processing methods to derive objective, quantitative measures of cortical thickness on a region-of-interest basis. We also generated 3D quantitative immunohistochemistry (qIHC) volumes of deposited ß-amyloid burden from 18month-old transgenic mice using an automated, production-level process. These studies revealed thinner cortex in most regions in the 1month-old transgenic mice relative to age-matched wild-types, with the exception of the frontal, perirhinal/entorhinal, posterior cingulate, and retrosplenial cortical regions. Between 1 and 3.5months-of-age, the transgenic mice demonstrated stable or increasing cortical thickness, while the wild-type mice showed cortical thinning. Based on data from co-registered 3D MRI and qIHC volumes, we identified an association between abnormal, early, regional cortical thickness change over 2.5months and later ß-amyloid deposition. These observations suggest that the spatio-temporal pattern of early (pre-plaque) alterations in cerebral cortical structure is indicative of regional predisposition to later ß-amyloid pathology in a transgenic AD mouse model.

Biallelic losses of 13q do not confer a poorer outcome in chronic lymphocytic leukaemia: analysis of 627 patients with isolated 13q deletion.

Losses in 13q as a sole abnormality confer a good prognosis in chronic lymphocytic leukaemia (CLL). Nevertheless, its heterogeneity has been demonstrated and the clinical significance of biallelic 13q deletions remains controversial. We compared the clinico-biological characteristics of a series of 627 patients harbouring isolated 13q deletions by fluorescence in situ hybridization (FISH), either monoallelic (13q × 1), biallelic (13q × 2), or the coexistence of both clones (13qM). The most frequent 13q deletion was 13q × 1 (82·1%), while 13q × 2 and 13qM represented 8·6% and 9·3% of patients respectively. The median percentage of altered nuclei significantly differed across groups: 55%, 72·5% and 80% in 13q × 1, 13q × 2 and 13qM (P < 0·001). However, no significant differences in the clinical outcome among 13q groups were found. From 84 patients with sequential FISH studies, eight patients lost the remaining allele of 13q whereas none of them changed from 13q × 2 to the 13q × 1 group. The percentage of abnormal cells detected by FISH had a significant impact on the five-year cumulative incidence of treatment and the overall survival, 90% being the highest predictive power cut-off. In conclusion, loss of the remaining 13q allele is not enough to entail a worse prognosis in CLL. The presence of isolated 13q deletion can be risk-stratified according to the percentage of altered cells.

Spatial association between distributed ß-amyloid and tau varies with cognition.

Several PET studies have explored the relationship between ß-amyloid load and tau uptake at the early stages of Alzheimer's disease (AD) progression. Most of these studies have focused on the linear relationship between ß-amyloid and tau at the local level and their synergistic effect on different AD biomarkers. We hypothesize that patterns of spatial association between ß-amyloid and tau might be uncovered using alternative association metrics that account for linear as well as more complex, possible nonlinear dependencies. In the present study, we propose a new Canonical Distance Correlation Analysis (CDCA) to generate distinctive spatial patterns of the cross-correlation structure between tau, as measured by [18F]flortaucipir PET, and ß-amyloid, as measured by [18F]florbetapir PET, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We found that the CDCA-based ß-amyloid scores were not only maximally distance-correlated to tau in cognitively normal (CN) controls and mild cognitive impairment (MCI), but also differentiated between low and high levels of ß-amyloid uptake. The most distinctive spatial association pattern was characterized by a spread of ß-amyloid covering large areas of the cortex and localized tau in the entorhinal cortex. More importantly, this spatial dependency varies according to cognition, which cannot be explained by the uptake differences in ß-amyloid or tau between CN and MCI subjects. Hence, the CDCA-based scores might be more accurate than the amyloid or tau SUVR for the enrollment in clinical trials of those individuals on the path of cognitive deterioration.

Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease.

Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.

Revealing the combined roles of Aß and tau in Alzheimer's disease via a pathophysiological activity decoder.

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, evidence in humans remains scarce, necessitating improved non-invasive techniques and integrative mechanistic models. Here, we introduce personalized brain activity models incorporating functional MRI, amyloid-ß (Aß) and tau-PET from AD-related participants (N=132). Within the model assumptions, electrophysiological activity is mediated by toxic protein deposition. Our integrative subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aß and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP). Furthermore, our results reproduce hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aß-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.