Диагностический потенциал методов анализа электроэнцефалограммы при болезни Альцгеймера

Электроэнцефалография обсуждается как один из из возможных инструментов для ранней диагностики и мониторинга течения болезни Альцгеймера. Проведен анализ 64 оригинальных работ (1998–2024), посвященных диагностике болезни Альцгеймера с применением спектрального и когерентного анализа электроэнцефалограммы, анализа вызванных потенциалов, а также теории графов. Было показано, что современные методы анализа электроэнцефалографических данных демонстрируют достаточно высокий потенциал для выявления нейрофизиологических маркеров болезни Альцгеймера. Так, спектральный анализ позволяет зафиксировать увеличение мощности дельтаи тета-ритмов наряду со снижением альфа-активности и индивидуальной альфа-частоты, что отражает нейродегенеративные процессы и нарушение когнитивной регуляции. Когерентный анализ указывает на дезинтеграцию функциональных связей между корковыми областями, проявляющуюся снижением когерентности в альфаи бета-диапазонах и компенсаторным повышением в медленных ритмах. Анализ вызванных потенциалов, особенно компонентов P300 и N400, свидетельствует о замедлении обработки информации и снижении эффективности когнитивных механизмов. Методы теории маломирных сетей дополняют эту картину, показывая нарушение баланса между локальной специализацией и глобальной интеграцией нейронных сетей. В свою очередь, применение алгоритмов машинного обучения на основе этих показателей открывает возможности для повышения точности диагностики, прогнозирования прогрессирования когнитивных нарушений и разработки автоматизированных систем раннего выявления болезни Альцгеймера. Таким образом, методы количественного анализа электроэнцефалограммы могут быть рассмотрены как один из возможных инструментов раннего скрининга болезни Альцгеймера, учитывая доказанные характерные паттерны мозговой активности. Однако для эффективного использования электроэнцефалограммы необходима стандартизация протоколов, унификация методов анализа и интеграция с алгоритмами машинного обучения.

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