Conditional Mutual Information Maps as Descriptors of Net Connectivity Levels in the Brain

There is a growing interest in finding ways to summarize the local connectivity properties of the brain through single brain maps. Here we propose a method based on the conditional mutual information (CMI) in the frequency domain. CMI maps quantify the amount of non-redundant covariability between each site and all others in the rest of the brain, partialling out the joint variability due to gross physiological noise. Average maps from a sample of 45 healthy individuals scanned in the resting state show a clear and symmetric pattern of connectivity maxima in several regions of cortex, including prefrontal, orbitofrontal, lateral–parietal, and midline default mode network components; and in subcortical nuclei, including the amygdala, thalamus, and basal ganglia. Such cortical and subcortical hotspots of functional connectivity were more clearly evident at lower frequencies (0.02–0.1 Hz) than at higher frequencies (0.1–0.2 Hz) of endogenous oscillation. CMI mapping can also be easily applied to perform group analyses. This is exemplified by exploring effects of normal aging on CMI in a sample of healthy controls and by investigating correlations between CMI and positive psychotic symptom scores in a sample of 40 schizophrenic patients. Both the normative aging and schizophrenia studies reveal functional connectivity trends that converge with reported findings from other studies, thus giving further support to the validity of the proposed method

Role of neurotrophins in depressive symptoms and executive function: Association analysis of NRN1 gene and its interaction with BDNF gene in a non-clinical sample

Background Neuritin-1 is a neurotrophic factor involved in synaptic plasticity that has been associated with depressive disorders, schizophrenia and cognitive performance. The study of genotype-phenotype relationships in healthy individuals is a useful framework to investigate the etiology of brain dysfunctions. We therefore aimed to investigate in a non-clinical sample whether NRN1 gene contributes to the psychopathological profile, with a particular focus on the clinical dimensions previously related to the NRN1 gene (i.e. depressive and psychotic). Furthermore, we aimed to analyze: i) the role of NRN1 on executive functions, ii) whether the association between either NRN1-psychopathological profile or NRN1-cognitive performance is moderated by the BDNF gene. Methods The sample is comprised of 410 non-clinical subjects who filled in the self-reported Brief Symptom Inventory (BSI) and were assessed for executive performance (Verbal Fluency, Wisconsin Card Sorting Test (WCST) and Letter-Number subscale (WAIS-III)). Genotyping included nine SNPs in NRN1 and one in BDNF. Results i) GG homozygotes (rs1475157-NRN1) showed higher scores on BSI depressive dimension and on total scores compared to A carriers (corrected p-values: 0.0004 and 0.0003, respectively). ii) A linear trend was detected between GG genotype of rs1475157 and a worse cognitive performance in WCST total correct responses (uncorrected p-value: 0.029). iii) Interaction between rs1475157-NRN1 and Val66Met-BDNF was found to modulate depressive symptoms (p=0.001, significant after correction). Limitations Moderate sample size; replication in a larger sample is needed. Conclusions NRN1 is associated with depressive symptoms and executive function in a non-clinical sample. Our results also suggest that the role of NRN1 seems to be modulated by BDNF.This study was supported by: i) Intramural Project CIBERSAM (P91E), ii) The Network of European Funding for Neuroscience Research, ERA-NET NEURON (PiM2010ERN-00642), iii) Instituto de Salud Carlos III through the project PI15/01420 (co-funded by European Regional Development Fund /European Social Fund, “Investing in your future”). Thanks to: i) the Comissionat per a Universitats i Recerca del DIUE (2014SGR1636), ii) Universitat de Barcelona and APIF-IBUB grant 2014. All funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data

Multivariate brain functional connectivity through regularized estimators

Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models

Facial Biomarkers Detect Gender-Specific Traits for Bipolar Disorder

Bipolar disorder (BD) is a psychiatric disorder associated with brain and neurodevelopmental alterations. As in other disorders, patients with BD present minor Physical Anomalies (MPAs) in higher frequency than healthy subjects. MPAs are subtle signs of developmental deviation that appear in body regions that share the ectodermal origin of the brain and are likely triggered by the same insults altering early brain development in mental disorders. MPAs are thus considered potential biomarkers for neurodevelopmental disorders. In this study, we compared facial shape variation between patients with BD and healthy controls using 3D facial reconstructions from magnetic resonance images (MRI) to test the potential of MPAs as a biomarker of BD diagnosis. Moreover, we assessed sex-specific facial shape variation to test whether the disorder affects differently male and female patients. We collected the 3D coordinates of 20 anatomical facial landmarks in a sample of 174 subjects (87 patients with BD and 87 healthy controls) and analyzed global and local patterns of facial shape using Geometric Morphometrics and multivariate statistical techniques. Although Procrustes-ANOVA analysis revealed that diagnosis accounted for a low but significant effect (1.1% of total facial shape variance, P-value=0.016), global facial shape did not significantly discriminate between patients with BD and healthy controls (P-value=0.19). However, Euclidean Distance Matrix Analysis (EDMA) based on local distances of the face revealed that 16.8% of facial traits were significantly different between patients with BD and healthy controls. Remarkably, the patterns of facial differences were sex-specific, suggesting that BD has a different effect on male and female patients. These findings show that local facial differences could be used as biomarkers for an improved diagnosis of BD and raise awareness on the importance of studying sex differences on neurodevelopmental disorders to develop more specific and efficient treatments

Converging Medial Frontal Resting State and Diffusion Based Abnormalities in Borderline Personality Disorder

Background The psychological profile of patients with borderline personality disorder (BPD), with impulsivity and emotional dysregulation as core symptoms, has guided the search for abnormalities in specific brain areas such as the hippocampal-amygdala complex and the frontomedial cortex. However, whole-brain imaging studies so far have delivered highly heterogeneous results involving different brain locations. Methods Functional resting-state and diffusion magnetic resonance imaging data were acquired in patients with BPD and in an equal number of matched control subjects (n = 60 for resting and n = 43 for diffusion). While mean diffusivity and fractional anisotropy brain images were generated from diffusion data, amplitude of low-frequency fluctuations and global brain connectivity images were used for the first time to evaluate BPD-related brain abnormalities from resting functional acquisitions. Results Whole-brain analyses using a p = .05 corrected threshold showed a convergence of alterations in BPD patients in genual and perigenual structures, with frontal white matter fractional anisotropy abnormalities partially encircling areas of increased mean diffusivity and global brain connectivity. Additionally, a cluster of enlarged amplitude of low-frequency fluctuations (high resting activity) was found involving part of the lefthippocampus and amygdala. In turn, this cluster showed increased resting functional connectivity with theanterior cingulate. Conclusions With a multimodal approach and without using a priori selected regions, we prove that structural and functional abnormality in BPD involves both temporolimbic and frontomedial structures as well as their connectivity. These structures have been previously related to behavioral and clinical symptoms in patients with BPD

High Potential of Facial Biomarkers to Diagnose Psychotic Disorders

Schizophrenia (SCZ) and Bipolar Disorder (BP) are severe psychiatric disorders (PD) that affect more than 3% of the world's population and are among the leading causes of disability worldwide. Current diagnostic systems represent these PD as independent categorical entities. However, recent studies propose that both disorders would be two different manifestations of the same psychotic spectrum continuum. Differential diagnosis is mainly based on their clinical presentation, and reliable biomarkers remain an unmet clinical need. Since the brain and the face are derived from the same ectodermal origins and their development is intimately integrated through common genetic signaling, facial biomarkers emerge as one of the most promising biological risk factors for PD. Here, we assessed the potential of facial anatomy in predicting the diagnosis of SCZ and BP. Analyses were performed in a sample of 180 adults distributed in three groups of BP patients (n=46), SCZ patients (n=67), and CNT (n=67) matched by age and premorbid IQ. Faces were manually annotated from reconstructions of magnetic resonance scans. Facial shape correctly discriminated patients with BP and SCZ, even when facial differences between patients and CNT were so subtle that are not recognizable to the untrained eye or by exploratory multivariate statistical techniques. After cross-validation, 62-65% of patients were correctly diagnosed based on face shape. This percentage is similar to the discriminatory power of other genetic and brain biomarkers. Using Artificial Neural Networks, we tested a machine learning algorithm based on facial morphology to diagnose SCZ. The overall accuracy in diagnostic classification was greater than 90%, whereas the precision ranged between 70-95% depending on the model. We also trained a Support Vector Machine classification algorithm to diagnose BP. Results showed that BP is harder to diagnose from facial biomarkers than SCZ, achieving a 72% accuracy. Euclidean Distance Matrix Analysis (EDMA) detected local facial differences involving the eyes, nose and mouth, and the relative separation/position between them. Facial anomalies were more abundant in SCZ patients, with 43-48% distances across the whole face significantly different from control subjects. In BP, the percentage of facial anomalies was lower, 24-32%, especially in women. Some facial differences were common to SCZ and BP, although the sense of change could be different among disorders. Remarkably, EDMA showed facial patterns that are disorder and gender-specific. These results demonstrate that an analysis of the spectrum of psychotic disorders under a gender perspective is crucial to further understand these disorders and to identify reliable biomarkers that can lead to early PD diagnosis

Autobiographical memory and default mode network function in schizophrenia : an fMRI study

The brain functional correlates of autobiographical recall are well established, but have been little studied in schizophrenia. Additionally, autobiographical memory is one of a small number of cognitive tasks that activates rather than de-activates the default mode network, which has been found to be dysfunctional in this disorder. Twenty-seven schizophrenic patients and 30 healthy controls underwent functional magnetic resonance imaging while viewing cue words that evoked autobiographical memories. Control conditions included both non-memory-evoking cues and a low level baseline (cross fixation). Compared to both non-memory evoking cues and low level baseline, autobiographical recall was associated with activation in default mode network regions in the controls including the medial frontal cortex, the posterior cingulate cortex and the hippocampus, as well as other areas. Clusters of de-activation were seen outside the default mode network. There were no activation differences between the schizophrenic patients and the controls, but the patients showed clusters of failure of de-activation in non-default mode network regions. According to this study, patients with schizophrenia show intact activation of the default mode network and other regions associated with recall of autobiographical memories. The finding of failure of de-activation outside the network suggests that schizophrenia may be associated with a general difficulty in de-activation rather than dysfunction of the default mode network per se

Combining fMRI and DISC1 gene haplotypes to understand working memory-related brain activity in schizophrenia

Altres ajuts: Ministerio de Ciencia e Innovación; Fondo Europeo de Desarrollo Regional (FEDER); European Social Fund ("Investing in your future"); Generalitat de Catalunya, Departament de Salut (SLT017/20/000233).The DISC1 gene is one of the most relevant susceptibility genes for psychosis. However, the complex genetic landscape of this locus, which includes protective and risk variants in interaction, may have hindered consistent conclusions on how DISC1 contributes to schizophrenia (SZ) liability. Analysis from haplotype approaches and brain-based phenotypes can contribute to understanding DISC1 role in the neurobiology of this disorder. We assessed the brain correlates of DISC1 haplotypes associated with SZ through a functional neuroimaging genetics approach. First, we tested the association of two DISC1 haplotypes, the HEP1 (rs6675281-1000731-rs999710) and the HEP3 (rs151229-rs3738401), with the risk for SZ in a sample of 138 healthy subjects (HS) and 238 patients. This approach allowed the identification of three haplotypes associated with SZ (HEP1-CTG, HEP3-GA and HEP3-AA). Second, we explored whether these haplotypes exerted differential effects on n-back associated brain activity in a subsample of 70 HS compared to 70 patients (diagnosis × haplotype interaction effect). These analyses evidenced that HEP3-GA and HEP3-AA modulated working memory functional response conditional to the health/disease status in the cuneus, precuneus, middle cingulate cortex and the ventrolateral and dorsolateral prefrontal cortices. Our results are the first to show a diagnosis-based effect of DISC1 haplotypes on working memory-related brain activity, emphasising its role in SZ