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.

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.

Multivariate genomic and transcriptomic determinants of imaging-derived personalized therapeutic needs in Parkinson's disease.

Due to the marked interpersonal neuropathologic and clinical heterogeneity of Parkinson's disease (PD), current interventions are not personalized and fail to benefit all patients. Furthermore, we continue to lack well-established methods and clinical tests to tailor interventions at the individual level in PD. Here, we identify the genetic determinants of individual-tailored treatment needs derived from longitudinal multimodal neuroimaging data in 294 PD patients (PPMI data). Advanced multivariate statistical analysis revealed that both genomic and blood transcriptomic data significantly explain (P < 0.01, FWE-corrected) the interindividual variability in therapeutic needs associated with dopaminergic, functional, and structural brain reorganization. We confirmed a high overlap between the identified highly predictive molecular pathways and determinants of levodopa clinical responsiveness, including well-known (Wnt signaling, angiogenesis, dopaminergic activity) and recently discovered (immune markers, gonadotropin-releasing hormone receptor) pathways/components. In addition, the observed strong correspondence between the identified genomic and baseline-transcriptomic determinants of treatment needs/response supports the genome's active role at the time of patient evaluation (i.e., beyond individual genetic predispositions at birth). This study paves the way for effectively combining genomic, transcriptomic and neuroimaging data for implementing successful individually tailored interventions in PD and extending our pathogenetic understanding of this multifactorial and heterogeneous disorder.

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.

Integrated transcriptomic and neuroimaging brain model decodes biological mechanisms in aging and Alzheimer's disease.

Both healthy aging and Alzheimer's disease (AD) are characterized by concurrent alterations in several biological factors. However, generative brain models of aging and AD are limited in incorporating the measures of these biological factors at different spatial resolutions. Here, we propose a personalized bottom-up spatiotemporal brain model that accounts for the direct interplay between hundreds of RNA transcripts and multiple macroscopic neuroimaging modalities (PET, MRI). In normal elderly and AD participants, the model identifies top genes modulating tau and amyloid-ß burdens, vascular flow, glucose metabolism, functional activity, and atrophy to drive cognitive decline. The results also revealed that AD and healthy aging share specific biological mechanisms, even though AD is a separate entity with considerably more altered pathways. Overall, this personalized model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying effective genetic targets for extending healthy aging and treating AD progression.

Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box.

Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.

Spatially Distributed Amyloid-ß Reduces Glucose Metabolism in Mild Cognitive Impairment.

BACKGROUND: Several positron emission tomography (PET) studies have explored the relationship between amyloid-ß (Aß), glucose metabolism, and the APOEɛ4 genotype. It has been reported that APOEɛ4, and not aggregated Aß, contributes to glucose hypometabolism in pre-clinical stages of Alzheimer's disease (AD) pathology. OBJECTIVE: We hypothesize that typical measurements of Aß taken either from composite regions-of-interest with relatively high burden actually cover significant patterns of the relationship with glucose metabolism. In contrast, spatially weighted measures of Aß are more related to glucose metabolism in cognitively normal (CN) aging and mild cognitive impairment (MCI). METHODS: We have generated a score of amyloid burden based on a joint singular value decomposition (SVD) of the cross-correlation structure between glucose metabolism, as measured by [18F]2-fluoro-2-deoxyglucose (FDG) PET, and Aß, as measured by [18F]florbetapir PET, from the Alzheimer's Disease Neuroimaging Initiative study. This SVD-based score reveals cortical regions where a reduced glucose metabolism is maximally correlated with distributed patterns of Aß. RESULTS: From an older population of CN and MCI subjects, we found that the SVD-based Aß score was significantly correlated with glucose metabolism in several cortical regions. Additionally, the corresponding Aß network has hubs that contribute to distributed glucose hypometabolism, which, in turn, are not necessarily foci of Aß deposition. CONCLUSIONS: Our approach uncovered hidden patterns of the glucose metabolism-Aß relationship. We showed that the SVD-based Aß score produces a stronger relationship with decreasing glucose metabolism than either APOEɛ4 genotype or global measures of Aß burden.

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.

Mathematical Modeling of Protein Misfolding Mechanisms in Neurological Diseases: A Historical Overview.

Protein misfolding refers to a process where proteins become structurally abnormal and lose their specific 3-dimensional spatial configuration. The histopathological presence of misfolded protein (MP) aggregates has been associated as the primary evidence of multiple neurological diseases, including Prion diseases, Alzheimer's disease, Parkinson's disease, and Creutzfeldt-Jacob disease. However, the exact mechanisms of MP aggregation and propagation, as well as their impact in the long-term patient's clinical condition are still not well understood. With this aim, a variety of mathematical models has been proposed for a better insight into the kinetic rate laws that govern the microscopic processes of protein aggregation. Complementary, another class of large-scale models rely on modern molecular imaging techniques for describing the phenomenological effects of MP propagation over the whole brain. Unfortunately, those neuroimaging-based studies do not take full advantage of the tremendous capabilities offered by the chemical kinetics modeling approach. Actually, it has been barely acknowledged that the vast majority of large-scale models have foundations on previous mathematical approaches that describe the chemical kinetics of protein replication and propagation. The purpose of the current manuscript is to present a historical review about the development of mathematical models for describing both microscopic processes that occur during the MP aggregation and large-scale events that characterize the progression of neurodegenerative MP-mediated diseases.

Patients with chronic lymphocytic leukemia and complex karyotype show an adverse outcome even in absence of TP53/ATM FISH deletions.

Genomic complexity identified by chromosome banding analysis (CBA) predicts a worse clinical outcome in CLL patients treated either with standard or new treatments. Herein, we analyzed the clinical impact of complex karyotypes (CK) with or without high-risk FISH deletions (ATM and/or TP53, HR-FISH) in a cohort of 1045 untreated MBL/CLL patients. In all, 99/1045 (9.5%) patients displayed a CK. Despite ATM and TP53 deletions were more common in CK (25% vs 7%; P < 0.001; 40% vs 5%; P < 0.001, respectively), only 44% (40/90) patients with TP53 deletions showed a CK. CK group showed a significant higher two-year cumulative incidence of treatment (48% vs 20%; P < 0.001), as well as a shorter overall survival (OS) (79 mo vs not reached; P < 0.001). When patients were categorized regarding CK and HR-FISH, those with both characteristics showed the worst median OS (52 mo) being clearly distinct from those non-CK and non-HR-FISH (median not reached), but no significant differences were detected between cases with only CK or HR-FISH. Both CK and TP53 deletion remained statistically significant in the multivariate analysis for OS. In conclusion, CK group is globally associated with advanced disease and poor prognostic markers. Further investigation in larger cohorts with CK lacking HR-FISH is needed to elucidate which mechanisms underlie the poor outcome of this subgroup.

Chronic lymphocytic leukemia with isochromosome 17q: An aggressive subgroup associated with TP53 mutations and complex karyotypes.

Although i(17q) [i(17q)] is frequently detected in hematological malignancies, few studies have assessed its clinical role in chronic lymphocytic leukemia (CLL). We recruited a cohort of 22 CLL patients with i(17q) and described their biological characteristics, mutational status of the genes TP53 and IGHV and genomic complexity. Furthermore, we analyzed the impact of the type of cytogenetic anomaly bearing the TP53 defect on the outcome of CLL patients and compared the progression-free survival (PFS) and overall survival (OS) of i(17q) cases with those of a group of 38 CLL patients harboring other 17p aberrations. We detected IGHV somatic hypermutation in all assessed patients, and TP53 mutations were observed in 71.4% of the cases. Patients with i(17q) were more commonly associated with complex karyotypes (CK) and tended to have a poorer OS than patients with other anomalies affecting 17p13 (median OS, 44 vs 120 months, P = 0.084). Regarding chromosomal alterations, significant differences in the median OS were found among groups (P = 0.044). In conclusion, our findings provide new insights regarding i(17q) in CLL and show a subgroup with adverse prognostic features.

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.

Oxidative imbalance in low/intermediate-1-risk myelodysplastic syndrome patients: The influence of iron overload.

OBJECTIVE: To assess the generation of reactive oxygen species (ROS) and the involvement of the main antioxidant pathways in low/intermediate-1-risk myelodysplastic syndromes (MDS) with iron overload (IOL). METHODS: We examined the levels of superoxide anion (O2-), hydrogen peroxide (H2O2), antioxidants (glutathione, GSH; superoxide dismutase, SOD; catalase, CAT; and glutathione peroxidase, GPx), mitochondrial membrane potential (ΔΨm), and by-products of oxidative damage (8-isoprostanes and 8-oxo-7,8-dihydro-2'-deoxyguanosine, 8-oxo-dG) in 42 MDS patients (28 without IOL at diagnosis, and 14 who developed IOL) and 20 healthy subjects. RESULTS: Patients with IOL showed higher O2- levels (39.4 MFI) than normal controls (22.7 MFI, p=0.0356) and patients at diagnosis (19.4 MFI, p=0.0049). Antioxidant systems, except SOD activity, exhibited significant changes in IOL patients with respect to controls (CAT: 7.1 vs 2.7nmol/ml/min, p=0.0023; GPx: 50.9 vs 76.4nmol/ml/min, p=0.0291; GSH: 50.2 vs 24.1 MFI, p=0.0060). Furthermore, mitochondrial dysfunction was only detected in IOL cases compared to controls (ΔΨm: 3.6 vs 6.4 MFI, p=0.0225). Finally, increased levels of 8-oxo-dG were detected in both groups of patients. CONCLUSION: Oxidative stress is an important but non-static phenomenon in MDS disease, whose status is influenced by, among other factors, the presence of injurious iron.

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.

Impact of azacitidine on red blood cell alloimmunisation in myelodysplastic syndrome.

BACKGROUND: The incidence of alloimmunisation in myelodysplastic syndromes (MDS) during the era of supportive treatment ranges from 9 to 56%. However, it is unknown if the widespread use of hypomethylating agents has changed the risk of immunisation. The aim of this study is to evaluate the impact of azacitidine (AZA) therapy on red blood cell (RBC) alloimmunisation in transfused patients with MDS, myelodysplastic syndromes/myeloproliferative syndromes (MDS/MPS) and secondary acute myeloid leukaemia (AML). MATERIAL AND METHODS: We have analysed retrospectively all patients with MDS, MDS/MPS and secondary AML from MDS, who received their first transfusion in our hospital between January 1995 and December 2014. We have assessed the impact of age, sex, RBC and platelets units transfused, and AZA treatment on developing alloantibodies. RESULTS: In our study, the number of RBC units transfused increased the risk of developing alloantibodies. However aging and the treatment with AZA were associated with a lower rate of alloimmunisation. DISCUSSION: Patients with MDS, MDS/MPS and secondary AML who received treatment with AZA developed RBC antibodies at a lower rate than control group. We suggest that aging and immunosuppression due to AZA therapy could develop an immunological tolerance with a weak response to allotransfusions.