TMS-EEG perturbation biomarkers for Alzheimer's disease patients classification.

The combination of TMS and EEG has the potential to capture relevant features of Alzheimer's disease (AD) pathophysiology. We used a machine learning framework to explore time-domain features characterizing AD patients compared to age-matched healthy controls (HC). More than 150 time-domain features including some related to local and distributed evoked activity were extracted from TMS-EEG data and fed into a Random Forest (RF) classifier using a leave-one-subject out validation approach. The best classification accuracy, sensitivity, specificity and F1 score were of 92.95%, 96.15%, 87.94% and 92.03% respectively when using a balanced dataset of features computed globally across the brain. The feature importance and statistical analysis revealed that the maximum amplitude of the post-TMS signal, its Hjorth complexity and the amplitude of the TEP calculated in the window 45-80 ms after the TMS-pulse were the most relevant features differentiating AD patients from HC. TMS-EEG metrics can be used as a non-invasive tool to further understand the AD pathophysiology and possibly contribute to patients' classification as well as longitudinal disease tracking.

Precuneus magnetic stimulation for Alzheimer's disease: a randomized, sham-controlled trial.

Repetitive transcranial magnetic stimulation (rTMS) is emerging as a non-invasive therapeutic strategy in the battle against Alzheimer's disease. Alzheimer's disease patients primarily show alterations of the default mode network for which the precuneus is a key node. Here, we hypothesized that targeting the precuneus with TMS represents a promising strategy to slow down cognitive and functional decline in Alzheimer's disease patients. We performed a randomized, double-blind, sham-controlled, phase 2, 24-week trial to determine the safety and efficacy of precuneus stimulation in patients with mild-to-moderate Alzheimer's disease. Fifty Alzheimer's disease patients were randomly assigned in a 1:1 ratio to either receive precuneus or sham rTMS (mean age 73.7 years; 52% female). The trial included a 24-week treatment, with a 2-week intensive course in which rTMS (or sham) was applied daily five times per week, followed by a 22-week maintenance phase in which stimulation was applied once weekly. The Clinical Dementia Rating Scale-Sum of Boxes was selected as the primary outcome measure, in which post-treatment scores were compared to baseline. Secondary outcomes included score changes in the Alzheimer's Disease Assessment Scale-Cognitive Subscale, Mini-Mental State Examination and Alzheimer's Disease Cooperative Study-Activities of Daily Living scale. Moreover, single-pulse TMS in combination with EEG was used to assess neurophysiological changes in precuneus cortical excitability and oscillatory activity. Our findings show that patients that received precuneus repetitive magnetic stimulation presented a stable performance of the Clinical Dementia Rating Scale-Sum of Boxes score, whereas patients treated with sham showed a worsening of their score. Compared with the sham stimulation, patients in the precuneus stimulation group also showed also significantly better performances for the secondary outcome measures, including the Alzheimer's Disease Assessment Scale-Cognitive Subscale, Mini-Mental State Examination and Alzheimer's Disease Cooperative Study-Activities of Daily Living scale. Neurophysiological results showed that precuneus cortical excitability remained unchanged after 24 weeks in the precuneus stimulation group, whereas it was significantly reduced in the sham group. Finally, we found an enhancement of local gamma oscillations in the group treated with precuneus stimulation but not in patients treated with sham. We conclude that 24 weeks of precuneus rTMS may slow down cognitive and functional decline in Alzheimer's disease. Repetitive TMS targeting the default mode network could represent a novel therapeutic approach in Alzheimer's disease patients.

Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification.

Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In this study, we propose an automatic method of determining the duration of TMS-induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations. A preliminary study is conducted in patients with Alzheimer's disease (AD). Three metrics for characterizing the strength and duration of TMS-evoked EEG (TEP) activity are proposed and their potential in identifying AD patients from healthy controls was investigated. A dataset of TMS-EEG recordings from 17 AD and 17 healthy controls (HC) was used in our analysis. A Random Forest classification algorithm was trained on the extracted TEP metrics and its performance is evaluated in a leave-one-subject-out cross-validation. The created model showed promising results in identifying AD patients from HC with an accuracy, sensitivity and specificity of 69.32%, 72.23% and 66.41%, respectively. Clinical relevance- Three preliminary metrics were proposed to quantify the strength and duration of the response to TMS on EEG data. The proposed metrics were successfully used to identify Alzheimer's disease patients from healthy controls. These results proved the potential of this approach which will provide additional diagnostic value.

Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease.

Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain features from individual TMS responses and average them over a low, medium and high density EEG electrode set. Within a leave-one-subject-out validation scenario, the best classification performance for AD vs. HC was obtained using a high-density electrode with a Random Forest classifier. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.82% respectively. Clinical relevance- TMS-EEG responses were successfully used to identify Alzheimer's disease patients from healthy controls.

Decreased Frontal Gamma Activity in Alzheimer Disease Patients.

OBJECTIVE: In Alzheimer disease (AD) animal models, synaptic dysfunction has recently been linked to a disorder of high-frequency neuronal activity. In patients, a clear relation between AD and oscillatory activity remains elusive. Here, we attempt to shed light on this relation by using a novel approach combining transcranial magnetic stimulation and electroencephalography (TMS-EEG) to probe oscillatory activity in specific hubs of the frontoparietal network in a sample of 60 mild-to-moderate AD patients. METHODS: Sixty mild-to-moderate AD patients and 21 age-matched healthy volunteers (HVs) underwent 3 TMS-EEG sessions to assess cortical oscillations over the left dorsolateral prefrontal cortex, the precuneus, and the left posterior parietal cortex. To investigate the relations between oscillatory activity, cortical plasticity, and cognitive decline, AD patients underwent a TMS-based neurophysiological characterization and a cognitive evaluation at baseline. The latter was repeated after 24 weeks to monitor clinical evolution. RESULTS: AD patients showed a significant reduction of frontal gamma activity as compared to age-matched HVs. In addition, AD patients with a more prominent decrease of frontal gamma activity showed a stronger impairment of long-term potentiation-like plasticity and a more pronounced cognitive decline at subsequent follow-up evaluation at 24 weeks. INTERPRETATION: Our data provide novel evidence that frontal lobe gamma activity is dampened in AD patients. The current results point to the TMS-EEG approach as a promising technique to measure individual frontal gamma activity in patients with AD. This index could represent a useful biomarker to predict disease progression and to evaluate response to novel pharmacological therapies. ANN NEUROL 2022;92:464-475.

Effects of Palmitoylethanolamide Combined with Luteoline on Frontal Lobe Functions, High Frequency Oscillations, and GABAergic Transmission in Patients with Frontotemporal Dementia.

BACKGROUND: Frontotemporal dementia (FTD) is a presenile neurodegenerative disease for which there is no effective pharmacological treatment. Recently, a link has been proposed between neuroinflammation and FTD. OBJECTIVE: Here, we aim to investigate the effects of palmitoylethanolamide (PEA) combined with luteoline (PEA-LUT), an endocannabinoid with anti-inflammatory and neuroprotective effects, on behavior, cognition, and cortical activity in a sample of FTD patients. METHODS: Seventeen patients with a diagnosis of probable FTD were enrolled. Cognitive and neurophysiological evaluations were performed at baseline and after 4 weeks of PEA-LUT 700 mg×2/day. Cognitive effects were assessed by Neuropsychiatric Inventory (NPI), Mini-Mental State Examination, Frontal Assessment Battery (FAB), Screening for Aphasia in Neurodegeneration, Activities of Daily Living-Instrumental Activities of Daily Living, and Frontotemporal Lobar Degeneration-modified Clinical Dementia Rating scale. To investigate in vivo neurophysiological effects of PEA-LUT, we used repetitive and paired-pulse transcranial magnetic stimulation (TMS) protocols assessing LTP-like cortical plasticity, short-interval intracortical inhibition, long-interval intracortical inhibition (LICI), and short-latency afferent inhibition. Moreover, we used TMS combined with EEG to evaluate the effects on frontal lobe cortical oscillatory activity. RESULTS: Treatment with PEA-LUT was associated with an improvement in NPI and FAB scores. Neurophysiological evaluation showed a restoration of LICI, in particular at ISI 100 ms, suggesting a modulation of GABA(B) activity. TMS-EEG showed a remarkable increase of TMS-evoked frontal lobe activity and of high-frequency oscillations in the beta/gamma range. CONCLUSION: PEA-LUT could reduce behavioral disturbances and improve frontal lobe functions in FTD patients through the modulation of cortical oscillatory activity and GABA(B)ergic transmission.