Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm.

We explore various sparse regularization techniques for analyzing fMRI data, such as the ℓ1 norm (often called LASSO in the context of a squared loss function), elastic net, and the recently introduced k-support norm. Employing sparsity regularization allows us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. In this work we consider sparse regularization in both the regression and classification settings. We perform experiments on fMRI scans from cocaine-addicted as well as healthy control subjects. We show that in many cases, use of the k-support norm leads to better predictive performance, solution stability, and interpretability as compared to other standard approaches. We additionally analyze the advantages of using the absolute loss function versus the standard squared loss which leads to significantly better predictive performance for the regularization methods tested in almost all cases. Our results support the use of the k-support norm for fMRI analysis and on the clinical side, the generalizability of the I-RISA model of cocaine addiction.

Methylphenidate enhances executive function and optimizes prefrontal function in both health and cocaine addiction.

Previous studies have suggested dopamine to be involved in error monitoring/processing, possibly through impact on reinforcement learning. The current study tested whether methylphenidate (MPH), an indirect dopamine agonist, modulates brain and behavioral responses to error, and whether such modulation is more pronounced in cocaine-addicted individuals, in whom dopamine neurotransmission is disrupted. After receiving oral MPH (20 mg) or placebo (counterbalanced), 15 healthy human volunteers and 16 cocaine-addicted individuals completed a task of executive function (the Stroop color word) during functional magnetic resonance imaging (fMRI). During MPH, despite not showing differences on percent accuracy and reaction time, all subjects committed fewer total errors and slowed down more after committing errors, suggestive of more careful responding. In parallel, during MPH all subjects showed reduced dorsal anterior cingulate cortex response to the fMRI contrast error>correct. In the cocaine subjects only, MPH also reduced error>correct activity in the dorsolateral prefrontal cortex (controls instead showed lower error>correct response in this region during placebo). Taken together, MPH modulated dopaminergically innervated prefrontal cortical areas involved in error-related processing, and such modulation was accentuated in the cocaine subjects. These results are consistent with a dopaminergic contribution to error-related processing during a cognitive control task.

Can a single brain region predict a disorder?

We perform prediction of diverse disorders (Cocaine Use, Schizophrenia and Alzheimers disease) in unseen subjects from brain fMRI. First, we show that for multi-subject prediction of simple cognitive states (e.g. motor vs. calculation and reading), voxels-as-features methods produce clusters that are similar for different leave-one-subject-out folds; while for group classification (e.g. cocaine addicted vs. control subjects), voxels are scattered and less stable. Therefore, we chose to use a single region per experimental condition and a majority vote classifier. Interestingly, our method outperforms state-of-the-art techniques. Our method can integrate multiple experimental conditions and successfully predict disorders in unseen subjects (leave-one-subjectout generalization accuracy: 89.3% and 90.9% for Cocaine Use, 96.4% for Schizophrenia and 81.5% for Alzheimers disease). Our experimental results not only span diverse disorders, but also different experimental designs (block design and event related tasks), facilities, magnetic fields (1.5Tesla, 3Tesla, 4Tesla) and speed of acquisition (interscan interval from 1600ms to 3500ms). We further argue that our method produces a meaningful low dimensional representation that retains discriminability.

Enhanced midbrain response at 6-month follow-up in cocaine addiction, association with reduced drug-related choice.

Drug addiction is characterized by dysregulated dopamine neurotransmission. Although dopamine functioning appears to partially recover with abstinence, the specific regions that recover and potential impact on drug seeking remain to be determined. Here we used functional magnetic resonance imaging (fMRI) to study an ecologically valid sample of 15 treatment-seeking cocaine addicted individuals at baseline and 6-month follow-up. At both study sessions, we collected fMRI scans during performance of a drug Stroop task, clinical self-report measures of addiction severity and behavioral measures of cocaine seeking (simulated cocaine choice); actual drug use in between the two study sessions was also monitored. At 6-month follow-up (compared with baseline), we predicted functional enhancement of dopaminergically innervated brain regions, relevant to the behavioral responsiveness toward salient stimuli. Consistent with predictions, whole-brain analyses revealed responses in the midbrain (encompassing the ventral tegmental area/substantia nigra complex) and thalamus (encompassing the mediodorsal nucleus) that were higher (and more positively correlated) at follow-up than baseline. Increased midbrain activity from baseline to follow-up correlated with reduced simulated cocaine choice, indicating that heightened midbrain activations in this context may be marking lower approach motivation for cocaine. Normalization of midbrain function at follow-up was also suggested by exploratory comparisons with active cocaine users and healthy controls (who were assessed only at baseline). Enhanced self-control at follow-up was suggested by a trend for the commonly hypoactive dorsal anterior cingulate cortex to increase response during a drug-related context. Together, these results suggest that fMRI could be useful in sensitively tracking follow-up outcomes in drug addiction.

Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task.

Anterior cingulate cortex (ACC) hypoactivations during cognitive demand are a hallmark deficit in drug addiction. Methylphenidate (MPH) normalizes cortical function, enhancing task salience and improving associated cognitive abilities, in other frontal lobe pathologies; however, in clinical trials, MPH did not improve treatment outcome in cocaine addiction. We hypothesized that oral MPH will attenuate ACC hypoactivations and improve associated performance during a salient cognitive task in individuals with cocaine-use disorders (CUD). In the current functional MRI study, we used a rewarded drug cue-reactivity task previously shown to be associated with hypoactivations in both major ACC subdivisions (implicated in default brain function) in CUD compared with healthy controls. The task was performed by 13 CUD and 14 matched healthy controls on 2 d: after ingesting a single dose of oral MPH (20 mg) or placebo (lactose) in a counterbalanced fashion. Results show that oral MPH increased responses to this salient cognitive task in both major ACC subdivisions (including the caudal-dorsal ACC and rostroventromedial ACC extending to the medial orbitofrontal cortex) in the CUD. These functional MRI results were associated with reduced errors of commission (a common impulsivity measure) and improved task accuracy, especially during the drug (vs. neutral) cue-reactivity condition in all subjects. The clinical application of such MPH-induced brain-behavior enhancements remains to be tested.

Task-specific functional brain geometry from model maps.

In this paper we propose model maps to derive and represent the intrinsic functional geometry of a brain from functional magnetic resonance imaging (fMRI) data for a specific task. Model maps represent the coherence of behavior of individual fMRI-measurements for a set of observations, or a time sequence. The maps establish a relation between individual positions in the brain by encoding the blood oxygen level dependent (BOLD) signal over a time period in a Markov chain. They represent this relation by mapping spatial positions to a new metric space, the model map. In this map the Euclidean distance between two points relates to the joint modeling behavior of their signals and thus the co-dependencies of the corresponding signals. The map reflects the functional as opposed to the anatomical geometry of the brain. It provides a quantitative tool to explore and study global and local patterns of resource allocation in the brain. To demonstrate the merit of this representation, we report quantitative experimental results on 29 fMRI time sequences, each with sub-sequences corresponding to 4 different conditions for two groups of individuals. We demonstrate that drug abusers exhibit lower differentiation in brain interactivity between baseline and reward related tasks, which could not be quantified until now.