Recovery of inhibitory control prefrontal cortex function in inpatients with heroin use disorder: a 15-week longitudinal fMRI study

Importance

Heroin addiction and related mortality impose a devastating toll on society, with little known about the neurobiology of this disease or its treatment. Poor inhibitory control is a common manifestation of prefrontal cortex (PFC) impairments in addiction, and its potential recovery following treatment is largely unknown in heroin (or any drug) addiction.

Objective

To study inhibitory control brain activity in iHUD and HC, before and after 15 weeks of inpatient treatment in the former.

Design

A longitudinal cohort study (11/2020-03/2022) where iHUD and HC underwent baseline and follow-up fMRI scans. Average follow-up duration: 15 weeks.

Setting

The iHUD and HC were recruited from treatment facilities and surrounding neighborhoods, respectively.

Participants

Twenty-six iHUD [40.6±10.1 years; 7 (29.2%) women] and 24 age-/sex-matched HC [41.1±9.9 years; 9 (37.5%) women].

Intervention

Following the baseline scan, inpatient iHUD continued to participate in a medically-assisted program for an average of 15 weeks (abstinence increased from an initial 183±236 days by 65±82 days). The HC were scanned at similar time intervals.

Main Outcomes and Measures

Behavioral performance as measured by the stop-signal response time (SSRT), target detection sensitivity (d', proportion of hits in go vs. false-alarms in stop trials), and brain activity (blood-oxygen level dependent signal differences) during successful vs. failed stops in the stop signal task.

Results

As we previously reported, at time 1 and as compared to HC, iHUD exhibited similar SSRT but impaired d' [t(38.7)=2.37, p=.023], and lower anterior and dorsolateral PFC (aPFC, dlPFC) activity (p<.001). Importantly, at time 2, there were significant gains in aPFC and dlPFC activity in the iHUD (group*session interaction, p=.002); the former significantly correlated with increases in d' specifically in iHUD (p=.012).

Conclusions and Relevance

Compared to HC, the aPFC and dlPFC impairments in the iHUD at time 1 were normalized at time 2, which was associated with individual differences in improvements in target detection sensitivity. For the first time in any drug addiction, these results indicate a treatment-mediated inhibitory control brain activity recovery. These neurobehavioral results highlight the aPFC and dlPFC as targets for intervention with a potential to enhance self-control recovery in heroin addiction.

Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment

BACKGROUND

Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors.

PURPOSE

Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance.

DATA SOURCES

Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

STUDY SELECTION

From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.

DATA ANALYSIS

Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool.

DATA SYNTHESIS

The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898-0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.

LIMITATIONS

Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.

CONCLUSIONS

Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.