Resting-state functional connectivity-based biomarkers and functional MRI-based neurofeedback for psychiatric disorders: a challenge for developing theranostic biomarkers

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., theranostic biomarker) is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce recent approach for creating a theranostic biomarker, which consists mainly of two parts: (i) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (ii) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.Comment: 46 pages, 5 figure

Biol Psychiatry Cogn Neurosci Neuroimaging

Background:Posttraumatic Stress Disorder (PTSD) is a debilitating disorder and there is no current accurate prediction of who develops it after trauma. Neurobiologically, individuals with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior cingulate), and these aberrations correlate with severity of illness. Prior small-scale research (n < 25) has also shown that hippocampal-rsFC measured acutely after trauma is predictive of future severity using an ROI-based approach. While a promising biomarker, to-date no study has employed a data-driven approach to test whole-brain hippocampal-FC patterns in forecasting the development of PTSD symptoms.Methods:Ninety-eight adults at risk of PTSD were recruited from the emergency department following traumatic injury and completed resting functional magnetic resonance imaging (rsfMRI; 8min) within 1-month; 6-months later they completed the Clinician-Administered PTSD Scale (CAPS-5) for assessment of PTSD symptom severity. Whole-brain rsFC values with bilateral hippocampi were extracted (CONN) and used in a machine learning kernel ridge regression analysis (PRoNTo); both a k-folds (k=10) and 70/30 testing vs. training split approach were used for cross-validation (1,000 iterations to bootstrap confidence intervals for significance values).Results:Acute hippocampal-rsFC significantly predicted CAPS-5 scores at 6-months (r=0.30, p=0.006; MSE=120.58, p=0.006; R2=0.09, p=0.025). In post-hoc analyses, hippocampal-rsFC remained significant after controlling for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity at 6-months (B=0.59, SE=0.20, p=0.003).Conclusions:Findings suggest functional connectivity of the hippocampus across the brain acutely after traumatic injury is associated with prospective PTSD symptom severity.TL1 TR001437/TR/NCATS NIH HHSUnited States/R01 MH106574/MH/NIMH NIH HHSUnited States/U01 CE002944/CE/NCIPC CDC HHSUnited States/UL1 TR001436/TR/NCATS NIH HHSUnited States/U01 NS088034/NS/NINDS NIH HHSUnited States

Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: A machine learning study

Background Posterior cingulate cortex (PCC) is a key aspect of the default mode network (DMN). Aberrant PCC functional connectivity (FC) is implicated in schizophrenia, but the potential for PCC related changes as biological classifier of schizophrenia has not yet been evaluated. Methods We conducted a data-driven approach using resting-state functional MRI data to explore differences in PCC-based region- and voxel-wise FC patterns, to distinguish between patients with first-episode schizophrenia (FES) and demographically matched healthy controls (HC). Discriminative PCC FCs were selected via false discovery rate estimation. A gradient boosting classifier was trained and validated based on 100 FES vs. 93 HC. Subsequently, classification models were tested in an independent dataset of 87 FES patients and 80 HC using resting-state data acquired on a different MRI scanner. Results Patients with FES had reduced connectivity between PCC and frontal areas, left parahippocampal regions, left anterior cingulate cortex, and right inferior parietal lobule, but hyperconnectivity with left lateral temporal regions. Predictive voxel-wise clusters were similar to region-wise selected brain areas functionally connected with PCC in relation to discriminating FES from HC subject categories. Region-wise analysis of FCs yielded a relatively high predictive level for schizophrenia, with an average accuracy of 72.28% in the independent samples, while selected voxel-wise connectivity yielded an accuracy of 68.72%. Conclusion FES exhibited a pattern of both increased and decreased PCC-based connectivity, but was related to predominant hypoconnectivity between PCC and brain areas associated with DMN, that may be a useful differential feature revealing underpinnings of neuropathophysiology for schizophrenia

Monitoring Self & World: A Novel Network Model of Hallucinations in Schizophrenia

Schizophrenia (Sz) is a psychotic disorder characterized by multifaceted symptoms including hallucinations (e.g. vivid perceptions that occur in the absence of external stimuli). Auditory hallucinations are the most common type of hallucination in Sz; roughly 70 percent of Sz patients report hearing voices specifically (e.g. auditory verbal hallucinations). Prior functional magnetic resonance imaging (fMRI) studies have provided initial insights into the neural mechanisms underlying hallucinations, implicating an anatomically-distributed network of cortical (sensory, insular, and inferior frontal cortex) and subcortical (hippocampal, striatal) regions. Yet, it remains unclear how this distributed network gives rise to hallucinations impacting different sensory modalities. The insular cortex is a central hub of a larger functional network called the salience network. By regulating default-mode network activity (associated with internally-directed thought), and fronto-parietal network activity (associated with externally-directed attention), the salience network is able to orient our attention to the most pressing matters (e.g. bodily pain, environmental threats, etc.). Abnormal salience monitoring is thought to underlie Sz symptoms; improper monitoring of salient internal events (e.g. auditory-verbal imagery, visual images) plausibly generates hallucinations, but no prior study has directly tested this hypothesis by exploring how sensory networks interact with the salience network in the context of hallucinations in Sz. This dissertation project combined exploratory and hypothesis-driven approaches to delineate functional neural markers of Sz symptoms. The first analysis explored the relationship between Sz symptom expression and altered functional communication between salience and default-mode networks. The second analysis explored fMRI signal fluctuations associated with modality-dependent (e.g. auditory, visual) hallucinations. The final analysis tested the hypothesis that abnormal functional communication between salience and sensory (e.g. auditory, visual) networks underlies hallucinations in Sz. The results suggest that there are three key players in the generation of auditory hallucinations in Sz: auditory cortex, hippocampus, and salience network. A novel functional network model of auditory hallucinations is proposed to account for these findings

Towards robust and replicable sex differences in the intrinsic brain function of autism.

BACKGROUND: Marked sex differences in autism prevalence accentuate the need to understand the role of biological sex-related factors in autism. Efforts to unravel sex differences in the brain organization of autism have, however, been challenged by the limited availability of female data. METHODS: We addressed this gap by using a large sample of males and females with autism and neurotypical (NT) control individuals (ABIDE; Autism: 362 males, 82 females; NT: 409 males, 166 females; 7-18 years). Discovery analyses examined main effects of diagnosis, sex and their interaction across five resting-state fMRI (R-fMRI) metrics (voxel-level Z > 3.1, cluster-level P < 0.01, gaussian random field corrected). Secondary analyses assessed the robustness of the results to different pre-processing approaches and their replicability in two independent samples: the EU-AIMS Longitudinal European Autism Project (LEAP) and the Gender Explorations of Neurogenetics and Development to Advance Autism Research. RESULTS: Discovery analyses in ABIDE revealed significant main effects of diagnosis and sex across the intrinsic functional connectivity of the posterior cingulate cortex, regional homogeneity and voxel-mirrored homotopic connectivity (VMHC) in several cortical regions, largely converging in the default network midline. Sex-by-diagnosis interactions were confined to the dorsolateral occipital cortex, with reduced VMHC in females with autism. All findings were robust to different pre-processing steps. Replicability in independent samples varied by R-fMRI measures and effects with the targeted sex-by-diagnosis interaction being replicated in the larger of the two replication samples-EU-AIMS LEAP. LIMITATIONS: Given the lack of a priori harmonization among the discovery and replication datasets available to date, sample-related variation remained and may have affected replicability. CONCLUSIONS: Atypical cross-hemispheric interactions are neurobiologically relevant to autism. They likely result from the combination of sex-dependent and sex-independent factors with a differential effect across functional cortical networks. Systematic assessments of the factors contributing to replicability are needed and necessitate coordinated large-scale data collection across studies

Functional connectivity alterations between default mode network and occipital cortex in patients with obsessive-compulsive disorder (OCD)

Altered brain network connectivity is a potential biomarker for obsessive-compulsive disorder (OCD). A meta-analysis of resting-state MRI studies by Gürsel et al. (2018) described altered functional connectivity in OCD patients within and between the default mode network (DMN), the salience network (SN), and the frontoparietal network (FPN), as well as evidence for aberrant fronto-striatal circuitry. Here, we tested the replicability of these meta-analytic rsfMRI findings by measuring functional connectivity during resting-state fMRI in a new sample of OCD patients (n = 24) and matched controls (n = 33). We performed seed-to-voxel analyses using 30 seed regions from the prior meta-analysis. OCD patients showed reduced functional connectivity between the SN and the DMN compared to controls, replicating previous findings. We did not observe significant group differences of functional connectivity within the DMN, SN, nor FPN. Additionally, we observed reduced connectivity between the visual network to both the DMN and SN in OCD patients, in particular reduced functional connectivity between lateral parietal seeds and the left inferior lateral occipital pole. Furthermore, the right lateral parietal seed (associated with the DMN) was more strongly correlated with a cluster in the right lateral occipital cortex and precuneus (a region partly overlapping with the Dorsal Attentional Network (DAN)) in patients. Importantly, this latter finding was positively correlated to OCD symptom severity. Overall, our study partly replicated prior meta-analytic findings, highlighting hypoconnectivity between SN and DMN as a potential biomarker for OCD. Furthermore, we identified changes between the SN and the DMN with the visual network. This suggests that abnormal connectivity between cortex regions associated with abstract functions (transmodal regions such as the DMN), and cortex regions associated with constrained neural processing (unimodal regions such as the visual cortex), may be important in OCD

A Unified Functional Network Target for Deep Brain Stimulation in Obsessive-Compulsive Disorder

BACKGROUND: Multiple deep brain stimulation (DBS) targets have been proposed for treating intractable obsessive-compulsive disorder (OCD). Here, we investigated whether stimulation effects of different target sites would be mediated by one common or several segregated functional brain networks. METHODS: First, seeding from active electrodes of 4 OCD patient cohorts (N = 50) receiving DBS to anterior limb of the internal capsule or subthalamic nucleus zones, optimal functional connectivity profiles for maximal Yale-Brown Obsessive Compulsive Scale improvements were calculated and cross-validated in leave-one-cohort-out and leave-one-patient-out designs. Second, we derived optimal target-specific connectivity patterns to determine brain regions mutually predictive of clinical outcome for both targets and others predictive for either target alone. Functional connectivity was defined using resting-state functional magnetic resonance imaging data acquired in 1000 healthy participants. RESULTS: While optimal functional connectivity profiles showed both commonalities and differences between target sites, robust cross-predictions of clinical improvements across OCD cohorts and targets suggested a shared network. Connectivity to the anterior cingulate cortex, insula, and precuneus, among other regions, was predictive regardless of stimulation target. Regions with maximal connectivity to these commonly predictive areas included the insula, superior frontal gyrus, anterior cingulate cortex, and anterior thalamus, as well as the original stereotactic targets. CONCLUSIONS: Pinpointing the network modulated by DBS for OCD from different target sites identified a set of brain regions to which DBS electrodes associated with optimal outcomes were functionally connected-regardless of target choice. On these grounds, we establish potential brain areas that could prospectively inform additional or alternative neuromodulation targets for obsessive-compulsive disorder

Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.

Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies