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The human amygdala is thought to play a pivotal role in the processing of emotionally significant sensory information. The major subdivisions of the human amygdala—the laterobasal group (LB), the superficial group (SF), and the centromedial group (CM)—have been anatomically delineated, but the functional response properties of these amygdala subregions in humans are still unclear. We combined functional MRI with cyto-architectonically defined probabilistic maps to analyze the response characteristics of amygdala subregions in subjects presented with auditory stimuli. We found positive auditory stimulation-related signal changes predominantly in probabilistically defined LB, and negative responses predominantly in SF and CM. In the left amygdala, mean response magnitude in the core area of LB with 90–100% assignment probability was significantly larger than in the core areas of SF and CM. These differences were observed for pleasant and unpleasant stimuli. Our findings reveal that the probabilistically defined anatomical subregions of the human amygdala show distinctive fMRI response patterns. The stronger auditory responses in LB as compared with SF and CM may reflect a predominance of auditory inputs to human LB, similar to many animal species in which the majority of sensory, including auditory, afferents project to this subdivision of the amygdala. Our study indicates that the intrinsic functional differentiation of the human amygdala may be probed using fMRI combined with probabilistic anatomical maps.
Predicting adult Attention Deficit Hyperactivity Disorder (ADHD) using vocal acoustic features
(2021)
Background: It is a key concern in psychiatric research to investigate objective measures to support and ultimately improve diagnostic processes. Current gold standard diagnostic procedures for attention deficit hyperactivity disorder (ADHD) are mainly subjective and prone to bias. Objective measures such as neuropsychological measures and EEG markers show limited specificity. Recent studies point to alterations of voice and speech production to reflect psychiatric symptoms also related to ADHD. However, studies investigating voice in large clinical samples allowing for individual-level prediction of ADHD are lacking. The aim of this study was to explore a role of prosodic voice measures as objective marker of ADHD.
Methods: 1005 recordings were analyzed from 387 ADHD patients, 204 healthy controls, and 100 clinical (psychiatric) controls. All participants (age range 18-59 years, mean age 34.4) underwent an extensive diagnostic examination according to gold standard methods and provided speech samples (3 min in total) including free and given speech. Paralinguistic features were calculated, and random forest based classifications were performed using a 10-fold cross-validation with 100 repetitions controlling for age, sex, and education. Association of voice features and ADHD-symptom severity assessed in the clinical interview were analyzed using random forest regressions.
Results and Conclusion ADHD was predicted with AUC = 0.76. The analysis of a non-comorbid sample of ADHD resulted in similar classification performance. Paralinguistic features were associated with ADHD-symptom severity as indicated by random forest regression. In female participants, particularly with age < 32 years, paralinguistic features showed the highest classification performance (AUC = 0.86).
Paralinguistic features based on derivatives of loudness and fundamental frequency seem to be promising candidates for further research into vocal acoustic biomarkers of ADHD. Given the relatively good performance in female participants independent of comorbidity, vocal measures may evolve as a clinically supportive option in the complex diagnostic process in this patient group.
Competing Interest Statement: EA participated and received payments in the national advisory board ADHD of Shire/Takeda. JL is co-founder and CTO of PeakProfiling GmbH. He created audio-features used in this study, that are intellectual property of PeakProfiling GmbH. FH received payments by PeakProfiling GmbH.
Clinical Trial: NCT01104623