Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

Abstract

Single photon emission computed tomography (SPECT) and Electroencephalography (EEG) have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity) in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD), 69 patients with depressive cognitive impairment (DCI), 71 patients with amnestic mild cognitive impairment (aMCI), and 41 patients with amnestic subjective cognitive complaints (aSCC). We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO)-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%), aMCI vs. AD (70%), and AD vs. DCI (100%), while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82%) and aMCI vs. DCI (90%). Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%). In general, accuracies were higher when measures of interaction (i.e., connectivity measures) were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become an integral part for early differential diagnosis of cognitive impairment.

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

“…We identified twenty-four studies which used neurophysiological imaging methods, only three of which investigated non-AD dementias including PD and FTD (Garn et al 2017;Ruffini et al 2019;Dottori et al 2017). The majority of the studies (n=21) used quantitative EEG while the remaining used either MEG (Furutani et al 2020), event-related potential EEG (Chapman et al 2011) or combined EEG with SPECT (Höller et al 2017). Although half (n=12) of these studies were published since 2018, this cohort of publications also included some of the earliest papers identified in this review starting in 2005 (Dauwels et al 2010;Cichocki et al 2005).…”

“…APOE4 status and cognitive scores) to train a classifier, then selected neuroimaging features for classification, showing better performance when neuroimaging data (grey matter density, amyloid burden, APOE4 status; r = -0.68) were used to predict individualised rate of cognitive decline in MCI, compared to cognitive predictors (depression, memory and executive function scores; r = -0.4) (Giorgio et al 2020). Similarly, three studies showed that SPECT is able to classify MCI and AD, but its predictive value for MCI conversion is improved when combined with other imaging modalities or cognitive assessments (Borroni et al 2006;Holler et al 2017;Habert et al 2011).…”

Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review

“…In 2017, Höller et al (22) combined quantitative markers from SPECT and electroencephalography (EGG) for differential diagnosis of disorders with amnestic symptoms. The classification was again accomplished by SVM algorithm.…”

Alte Methoden: Der heutige Stellenwert der Elektroenzephalografie (EEG)

“…Die Schwere einer Enzephalopathie lässt sich mittels EEG gut bestimmen. Die Kombination nuklearmedizinischer Methoden (SPECT) und quantitativem EEG verbessert die Differenzierung zwischen neurodegenerativ und depressiv bedingten Gedächtnisbeschwerden [28].…”