CORRELATIONS BETWEEN DIFFUSION PARAMETERS AND NEUROMETABOLIC STATUS IN THE LEFT FRONTAL LOBE IN UHR SUBJECTS.

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

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An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing

Clinical EEG and Neuroscience ◽

10.1177/15500594211063662 ◽

2021 ◽

pp. 155005942110636

Author(s):

Francesco Carlo Morabito ◽

Cosimo Ieracitano ◽

Nadia Mammone

Keyword(s):

Artificial Intelligence ◽

Frontal Lobe ◽

Parietal Lobe ◽

Classification Performance ◽

Head Region ◽

Left Frontal Lobe ◽

Performance Accuracy ◽

Power Spectral ◽

Explainable Artificial Intelligence

An explainable Artificial Intelligence (xAI) approach is proposed to longitudinally monitor subjects affected by Mild Cognitive Impairment (MCI) by using high-density electroencephalography (HD-EEG). To this end, a group of MCI patients was enrolled at IRCCS Centro Neurolesi Bonino Pulejo of Messina (Italy) within a follow-up protocol that included two evaluations steps: T0 (first evaluation) and T1 (three months later). At T1, four MCI patients resulted converted to Alzheimer’s Disease (AD) and were included in the analysis as the goal of this work was to use xAI to detect individual changes in EEGs possibly related to the degeneration from MCI to AD. The proposed methodology consists in mapping segments of HD-EEG into channel-frequency maps by means of the power spectral density. Such maps are used as input to a Convolutional Neural Network (CNN), trained to label the maps as “T0” (MCI state) or “T1” (AD state). Experimental results reported high intra-subject classification performance (accuracy rate up to 98.97% (95% confidence interval: 98.68–99.26)). Subsequently, the explainability of the proposed CNN is explored via a Grad-CAM approach. The procedure allowed to detect which EEG-channels (i.e., head region) and range of frequencies (i.e., sub-bands) resulted more active in the progression to AD. The xAI analysis showed that the main information is included in the delta sub-band and that, limited to the analyzed dataset, the highest relevant areas are: the left-temporal and central-frontal lobe for Sb01, the parietal lobe for Sb02, the left-frontal lobe for Sb03 and the left-frontotemporal region for Sb04.

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Semantic monitoring of words with emotional connotation during fMRI: Contribution of anterior left frontal cortex

Journal of the International Neuropsychological Society ◽

10.1017/s1355617702801394 ◽

2002 ◽

Vol 8 (5) ◽

pp. 607-622 ◽

Cited By ~ 32

Author(s):

Bruce Crosson ◽

M. Allison Cato ◽

Joseph R. Sadek ◽

Didem Gökçay ◽

Russell M. Bauer ◽

...

Keyword(s):

Frontal Cortex ◽

Frontal Lobe ◽

Semantic Processing ◽

Normal Subjects ◽

Neural Substrates ◽

Significant Activity ◽

Left Frontal Lobe ◽

Temporal Lobes ◽

Semantic Attribute ◽

Animal Names

AbstractPrevious studies showed that cortex in the anterior portions of the left frontal and temporal lobes participates in generating words with emotional connotations and processing pictures with emotional content. If these cortices process the semantic attribute of emotional connotation, they should be active whenever processing emotional connotation, without respect to modality of input or mode of output. Thus, we hypothesized that they would activate during monitoring of words with emotional connotations. Sixteen normal subjects performed semantic monitoring of words with emotional connotations, animal names, and implement names during fMRI. Cortex in the anterior left frontal lobe demonstrated significant activity for monitoring words with emotional connotations compared to monitoring tone sequences, animal names, or implement names. Together, the current and previous results implicate cortex in the anterior left frontal lobe in semantic processing of emotional connotation, consistent with connections of this cortex to paralimbic association areas. Current findings also indicate that neural substrates for processing emotional connotation are independent of substrates for processing the categories of living and nonliving things.

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