Multivariate empirical mode decomposition reveals markers of Alzheimer’s Disease in the oscillatory response to transcranial magnetic stimulation
To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer’s Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands. We employed multivariat...
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| Published in | Clinical neurophysiology Vol. 176; p. 2110756 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.08.2025
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| Online Access | Get full text |
| ISSN | 1388-2457 1872-8952 1872-8952 |
| DOI | 10.1016/j.clinph.2025.2110756 |
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| Abstract | To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer’s Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands.
We employed multivariate empirical mode decomposition (MEMD) to analyze the TMS-EEG response of 38 AD patients and 21 control subjects. We used the distinct features of EEG oscillatory modes to train a classification algorithm, a support vector machine.
AD patients exhibited a weakened slow-frequency response. Faster oscillatory modes displayed a biphasic response pattern in controls, characterized by an early increase followed by a widespread suppression, which was reduced in AD patients. Classification achieved robust discrimination performance (85%/23% true/false positive rate).
AD causes an impairment in the oscillatory response to TMS that has distinct features in different frequency ranges. These features uncovered by MEMD could serve as an effective EEG diagnostic marker.
Early detection of AD requires diagnostic tools that are both effective and accessible. Combining EEG with TMS shows great promise. Our results and method enhance TMS-EEG both as a practical diagnostic tool, and as a way to further our understanding of AD pathophysiology.
•New EEG markers of Alzheimer’s Disease in the response to transcranial magnetic stimulation of the dorsolateral prefrontal cortex.•Impairment in early low-frequency and late high-frequency responses observed in Alzheimer’s Disease patients.•Robust algorithmic discrimination between patients and controls based on neurobiologically interpretable markers. |
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| AbstractList | To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer's Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands.
We employed multivariate empirical mode decomposition (MEMD) to analyze the TMS-EEG response of 38 AD patients and 21 control subjects. We used the distinct features of EEG oscillatory modes to train a classification algorithm, a support vector machine.
AD patients exhibited a weakened slow-frequency response. Faster oscillatory modes displayed a biphasic response pattern in controls, characterized by an early increase followed by a widespread suppression, which was reduced in AD patients. Classification achieved robust discrimination performance (85%/23% true/false positive rate).
AD causes an impairment in the oscillatory response to TMS that has distinct features in different frequency ranges. These features uncovered by MEMD could serve as an effective EEG diagnostic marker.
Early detection of AD requires diagnostic tools that are both effective and accessible. Combining EEG with TMS shows great promise. Our results and method enhance TMS-EEG both as a practical diagnostic tool, and as a way to further our understanding of AD pathophysiology. To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer's Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands.OBJECTIVETo investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer's Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands.We employed multivariate empirical mode decomposition (MEMD) to analyze the TMS-EEG response of 38 AD patients and 21 control subjects. We used the distinct features of EEG oscillatory modes to train a classification algorithm, a support vector machine.METHODSWe employed multivariate empirical mode decomposition (MEMD) to analyze the TMS-EEG response of 38 AD patients and 21 control subjects. We used the distinct features of EEG oscillatory modes to train a classification algorithm, a support vector machine.AD patients exhibited a weakened slow-frequency response. Faster oscillatory modes displayed a biphasic response pattern in controls, characterized by an early increase followed by a widespread suppression, which was reduced in AD patients. Classification achieved robust discrimination performance (85%/23% true/false positive rate).RESULTSAD patients exhibited a weakened slow-frequency response. Faster oscillatory modes displayed a biphasic response pattern in controls, characterized by an early increase followed by a widespread suppression, which was reduced in AD patients. Classification achieved robust discrimination performance (85%/23% true/false positive rate).AD causes an impairment in the oscillatory response to TMS that has distinct features in different frequency ranges. These features uncovered by MEMD could serve as an effective EEG diagnostic marker.CONCLUSIONSAD causes an impairment in the oscillatory response to TMS that has distinct features in different frequency ranges. These features uncovered by MEMD could serve as an effective EEG diagnostic marker.Early detection of AD requires diagnostic tools that are both effective and accessible. Combining EEG with TMS shows great promise. Our results and method enhance TMS-EEG both as a practical diagnostic tool, and as a way to further our understanding of AD pathophysiology.SIGNIFICANCEEarly detection of AD requires diagnostic tools that are both effective and accessible. Combining EEG with TMS shows great promise. Our results and method enhance TMS-EEG both as a practical diagnostic tool, and as a way to further our understanding of AD pathophysiology. To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer’s Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands. We employed multivariate empirical mode decomposition (MEMD) to analyze the TMS-EEG response of 38 AD patients and 21 control subjects. We used the distinct features of EEG oscillatory modes to train a classification algorithm, a support vector machine. AD patients exhibited a weakened slow-frequency response. Faster oscillatory modes displayed a biphasic response pattern in controls, characterized by an early increase followed by a widespread suppression, which was reduced in AD patients. Classification achieved robust discrimination performance (85%/23% true/false positive rate). AD causes an impairment in the oscillatory response to TMS that has distinct features in different frequency ranges. These features uncovered by MEMD could serve as an effective EEG diagnostic marker. Early detection of AD requires diagnostic tools that are both effective and accessible. Combining EEG with TMS shows great promise. Our results and method enhance TMS-EEG both as a practical diagnostic tool, and as a way to further our understanding of AD pathophysiology. •New EEG markers of Alzheimer’s Disease in the response to transcranial magnetic stimulation of the dorsolateral prefrontal cortex.•Impairment in early low-frequency and late high-frequency responses observed in Alzheimer’s Disease patients.•Robust algorithmic discrimination between patients and controls based on neurobiologically interpretable markers. AbstractObjective:To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer’s Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands. Methods:We employed multivariate empirical mode decomposition (MEMD) to analyze the TMS-EEG response of 38 AD patients and 21 control subjects. We used the distinct features of EEG oscillatory modes to train a classification algorithm, a support vector machine. Results:AD patients exhibited a weakened slow-frequency response. Faster oscillatory modes displayed a biphasic response pattern in controls, characterized by an early increase followed by a widespread suppression, which was reduced in AD patients. Classification achieved robust discrimination performance (85%/23% true/false positive rate). Conclusions:AD causes an impairment in the oscillatory response to TMS that has distinct features in different frequency ranges. These features uncovered by MEMD could serve as an effective EEG diagnostic marker. Significance:Early detection of AD requires diagnostic tools that are both effective and accessible. Combining EEG with TMS shows great promise. Our results and method enhance TMS-EEG both as a practical diagnostic tool, and as a way to further our understanding of AD pathophysiology. |
| ArticleNumber | 2110756 |
| Author | Rocchi, Lorenzo Papo, David Bernardi, Davide Fadiga, Luciano Casula, Elias P. Koch, Giacomo |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40516387$$D View this record in MEDLINE/PubMed |
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| Keywords | ADCS - ADL NPPT AD IMF Alzheimer’s Disease Transcranial magnetic stimulation DLPFC EEG FAB NPI MSO MEMD AM MEG CDR ADAS - COG SCD Brain oscillations MMSE RMT HC TMS Multivariate empirical mode decomposition TEP Intrinsic Mode Function Neuropsychiatric Inventory Clinical Dementia Rating Electroencephalography Transcranial Magnetic Stimulation Frontal Assessment Battery Non-parametric permutation test Multivariate Empirical Mode Decomposition Amplitude Modulation Alzheimer’s Disease Assessment Scale - Cognitive Subscale Scalp-to-cortex Distance TMS-evoked potential Magnetoencephalography Healthy Control Alzheimer’s Disease Cooperative Study - Activities of Daily Living Maximum Stimulator Output Resting Motor Threshold Dorsolateral Prefrontal Cortex Mini-Mental State Examination |
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| Snippet | To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer’s Disease (AD) patients and... AbstractObjective:To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer’s Disease... To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer's Disease (AD) patients and... |
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| SubjectTerms | Aged Aged, 80 and over Alzheimer Disease - diagnosis Alzheimer Disease - physiopathology Alzheimer’s Disease Brain oscillations EEG Electroencephalography - methods Female Humans Male Middle Aged Multivariate Analysis Multivariate empirical mode decomposition Neurology Support Vector Machine Transcranial magnetic stimulation Transcranial Magnetic Stimulation - methods |
| Title | Multivariate empirical mode decomposition reveals markers of Alzheimer’s Disease in the oscillatory response to transcranial magnetic stimulation |
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