Densely sampled neuroimaging for maximizing clinical insight in psychiatric and addiction disorders

Quality over quantity: powering neuroimaging samples in psychiatry

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.

Ketogenic diet reduces a neurobiological craving signature in inpatients with alcohol use disorder

Background and aims

Increasing evidence suggests that a ketogenic (high-fat, low-carbohydrate) diet (KD) intervention reduces alcohol withdrawal severity and alcohol craving in individuals with alcohol use disorder (AUD) by shifting brain energetics from glucose to ketones. We hypothesized that the KD would reduce a neurobiological craving signature when individuals undergoing alcohol detoxification treatment were exposed to alcohol cues.

Methods

We performed a secondary analysis of functional magnetic resonance data of 33 adults with an AUD who were randomized to a KD (n = 19) or a standard American diet (SA; n = 14) and underwent 3 weeks of inpatient alcohol detoxification treatment. Once per week, participants performed an alcohol cue-reactivity paradigm with functional magnetic resonance imaging. We extracted brain responses to food and alcohol cues and quantified the degree to which each set of brain images shared a pattern of activation with a recently established 'Neurobiological Craving Signature' (NCS). We then performed a group-by-time repeated measures ANOVA to test for differences in craving signature expression between the dietary groups over the three-week treatment period. We also correlated these expression patterns with self-reported wanting ratings for alcohol cues.

Results

For alcohol relative to food cues, there was a main effect of group, such that the KD group showed lower NCS expression across all 3 weeks of treatment. The main effect of time and the group-by-time interaction were not significant. Self-reported wanting for alcohol cues reduced with KD compared to SA but did not correlate with the NCS score.

Conclusion

A ketogenic diet reduces self-reported alcohol wanting, and induced lower NCS to alcohol cues during inpatient treatment for AUD. However, in the KD group alcohol wanting continued to decrease across the 3 weeks of abstinence while the NCS scores remained stable, suggesting that this cue-induced NCS may not fully capture ongoing, non-cue-induced alcohol desire.

Ketogenic diet reduces a neurobiological craving signature in alcohol use disorder

Background and Aims

Increasing evidence suggests that a ketogenic (high-fat, low-carbohydrate) diet intervention reduces alcohol withdrawal severity and alcohol craving in individuals with alcohol use disorder (AUD) by shifting brain energetics from glucose to ketones. We hypothesized that the ketogenic diet would reduce a brain craving signature when individuals undergoing alcohol detoxification treatment were exposed to alcohol cues.

Methods

We performed a secondary analysis of functional magnetic resonance data of n=33 adults with an AUD were randomized to a ketogenic diet (n=19) or a standard American diet (n=14) and underwent three weeks of inpatient alcohol detoxification treatment. Once per week, participants performed an alcohol cue-reactivity paradigm with functional magnetic resonance imaging. We extracted brain responses to food and alcohol cues and quantified the degree to which each set of brain images shared a pattern of activation with a recently validated 'Neurobiological Craving Signature' (NCS). We then performed a group-by-time repeated measures ANOVA to test for differences in craving signature expression between the dietary groups over the three-week treatment period. We also correlated these expression patterns with self-reported wanting ratings for alcohol cues.

Results

For alcohol relative to food cues, there was a main effect of group, such that the ketogenic diet group showed lower NCS expression across all three weeks of treatment. The main effect of time and the group-by-time interaction were not significant. Self-reported wanting for alcohol cues reduced with KD compared to SA but did not correlate with the NCS score.

Conclusions

A ketogenic diet reduces self-reported alcohol wanting, and induced lower brain craving signatures to alcohol cues during inpatient treatment for AUD.

Speak and you shall predict: speech at initial cocaine abstinence as a biomarker of long-term drug use behavior

Importance

Valid biomarkers that can predict longitudinal clinical outcomes at low cost are a holy grail in psychiatric research, promising to ultimately be used to optimize and tailor intervention and prevention efforts.

Objective

To determine if baseline linguistic markers in natural speech, as compared to non-speech clinical and demographic measures, can predict drug use severity measures at future sessions in initially abstinent individuals with cocaine use disorder (iCUD).

Design

A longitudinal cohort study (August 2017 - March 2020), where baseline measures were used to predict outcomes collected at three-month intervals for up to one year of follow-up.

Participants

Eighty-eight initially abstinent iCUD were studied at baseline; 57 (46 male, age 50.7+/-7.9 years) came back for at least another session.

Main Outcomes and Measures

Outcomes were self-reported symptoms of withdrawal, craving, abstinence duration and frequency of cocaine use in the past 90 days at each study session. The predictors were derived from 5-min recordings of vocal descriptions of the positive consequences of abstinence and the negative consequences of using cocaine; the baseline cocaine and other common drug use measures, demographic and neuropsychological variables were used for comparison.

Results

Models using the non-speech variables showed the best predictive performance at three(r>0.45, P<2×10-3) and six months follow-up (r>0.37, P<3×10-2). At 12 months, the natural language processing-based model showed significant correlations with withdrawal (r=0.43, P=3×10-2), craving (r=0.72, P=5×10-5), days of abstinence (r=0.76, P=1×10-5), and cocaine use in the past 90 days (r=0.61, P=2×10-3), significantly outperforming the other models for abstinence prediction.

Conclusions and Relevance

At short time intervals, maximal predictive power was obtained with models that used baseline drug use (in addition to demographic and neuropsychological) measures, potentially reflecting a slow rate of change in these measures, which could be estimated by linear functions. In contrast, short speech samples predicted longer-term changes in drug use, implying deeper penetrance by potentially capturing non-linear dynamics over longer intervals. Results suggest that, compared to the common outcome measures used in clinical trials, speech-based measures could be leveraged as better predictors of longitudinal drug use outcomes in initially abstinent iCUD, as potentially generalizable to other substance use disorders and related comorbidity.

From Computation to Clinic

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.