This chapter covers the role of brain oscillations in shaping the microarchitecture of sleep and their role in physiological and pathological states. Neural oscillations (delta to gamma frequencies) are fundamental to the structure of sleep stages and play a central role in cognitive and restorative processes of the human brain. This chapter discusses how different oscillatory signatures characterize non-rapid eye movement (NREM) and rapid eye movement (REM) sleep, with slow waves dominating deep sleep and desynchronization in REM sleep. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are complementary to investigate sleep dynamics. EEG offers high temporal resolution in terms of oscillatory activity, and fMRI provides a spatial mapping of the network. The convergence of these modalities has provided new insights into sleep spindles, slow oscillations, and their association with large functional connections. Also emphasized in the chapter are the quantitative EEG metrics and advances in signal processing and machine learning that can help identify sleep stages and pathological patterns. The disruption of oscillatory dynamics in sleep disorders and neurodegenerative conditions is also explored as oscillatory biomarkers can be used as diagnostic and prognostic tools. Overall, the chapter highlights the implications of oscillatory dynamics as a common framework to connect sleep physiology with cognitive function and clinical applications, highlighting the potential for multimodal neuroimaging.
This chapter explores the neural mechanisms and architectural organization of sleep, which is essential for memory consolidation, neural plasticity, metabolic homeostasis, and cognitive function. Sleep is regulated by an integrated system of homeostatic sleep pressure and circadian rhythms that is linked to distributed neural networks such as the hypothalamus, brainstem, thalamus, basal forebrain, and cerebral cortex. Control of sleep is mediated through the balance between sleep-promoting networks (e.g. ventrolateral preoptic nucleus and its GABAergic projections) and arousal-promoting (e.g. orexinergic, cholinergic, monoaminergic, and histaminergic networks). This chapter looks at sleep from the macro and micro points of view and emphasizes the distinct neurophysiological attributes and functions of NREM and REM sleep. We focus on slow-wave activity, sleep spindles, and thalamocortical oscillations as electrophysiological substrates for synaptic plasticity, memory consolidation, and large-scale network synchronization. Research in polysomnography, neuroimaging, computational neuroscience, and artificial intelligence has also shed light on the neural architecture of sleep and its role in cognition, emotional regulation, and brain health. All in all, sleep is discovered as an emergent property of integrated neural systems that has a fundamental role to play in neuropsychiatric, metabolic, and neurodegenerative disorders and further scope for translational and clinical sleep investigations.
This chapter examines the intricate relationship between sleep and higher-order cognitive functions, particularly attention and executive control. Sleep is presented as a critical modulator of cognitive performance, influencing neural systems that underpin attentional regulation, working memory, and decision-making. The chapter highlights the vulnerability of attention to sleep deprivation, with impairments in sustained attention and processing efficiency linked to disruptions in dopaminergic and noradrenergic systems. The chapter also examines how sleep parameters and sleep architecture affect ADHD pathophysiology and symptomatology. Executive functions, including cognitive flexibility and inhibitory control, are shown to depend heavily on the integrity of prefrontal cortical networks, which are particularly sensitive to sleep disturbances. Neurobiological mechanisms underlying these relationships are explored, including thalamocortical circuitry, cholinergic modulation, and neuroendocrine influences such as cortisol dysregulation. Structural and functional neuroimaging studies reveal that insufficient sleep is associated with reduced prefrontal activity and altered connectivity, contributing to observable cognitive deficits. The chapter also discusses clinical implications, emphasizing the role of sleep interventions such as cognitive behavioral therapy in enhancing sleep quality, cognitive outcomes, and ADHD management. The bidirectional relationship between sleep and cognition is underscored, positioning sleep as a modifiable factor in optimizing cognitive resilience and mitigating neuropsychiatric risk.
This chapter synthesizes current evidence on the role of sleep in memory consolidation, emphasizing the interplay between neural oscillations, synaptic plasticity, and cognitive processes. Sleep is conceptualized as an active state, facilitating the encoding, stabilization, and integration of memories into long-term storage. Distinct contributions of sleep stages are highlighted, with slow-wave sleep (SWS) supporting declarative memory consolidation through hippocampal-neocortical replay mechanisms, and Rapid eye movement (REM) sleep enhancing procedural and emotional memory processing. The synaptic homeostasis hypothesis is discussed as a central theoretical framework, proposing that sleep downscales synaptic strength while preserving salient memory traces, thereby optimizing neural efficiency and learning capacity. The chapter further explores the role of sleep spindles, delta oscillations, and high-frequency activity in mediating synaptic plasticity and memory reorganization. Neuroimaging evidence demonstrates coordinated activity between hippocampal and cortical networks during sleep, reinforcing the concept of memory replay and systems consolidation. Clinical implications are addressed, including the impact of sleep deprivation on cognitive performance and the potential for sleep-based interventions to enhance learning and mitigate cognitive decline. The chapter concludes by emphasizing the bidirectional relationship between sleep and memory, underscoring the importance of sleep quality in maintaining cognitive health across the lifespan.
This chapter includes an extensive examination of the neuroanatomical and neurophysiological basis of dreaming and is primarily concerned with the phenomenology of rapid eye movement (REM) sleep. Relying on integrated findings from neuroimaging, electrophysiology, and neurochemical studies, the chapter describes that dreaming results from the interaction of brain regions such as the brainstem, thalamus, limbic system, and prefrontal cortex. The chapter underscores the central contribution of pontine cholinergic mechanisms to the generation of REM, combined with thalamocortical integration, to define perceptual and narrative features of dreams. Limbic activation in the amygdala and hippocampus is responsible for the emotional intensity and memory-related features of dream content, and relative prefrontal deactivation helps minimize logical constraint and promotes associative reasoning. Oscillatory dynamics, particularly theta and gamma activity, are considered important underpinnings of dream vividness and perceptual integration. With its focus on clinical conditions, including post-traumatic stress disorder (PTSD), depression, and REM sleep behavior disorder, the chapter provides a translational perspective beyond simple mechanistic models associated with the brain. The modulation of acetylcholine, serotonin, and norepinephrine is proposed to be one of the most important mechanisms of regulation with therapeutic potential. The chapter also addresses new domains such as dream neuropharmacology, neuromodulation, ethical implications, and cross-cultural perspectives of dream interpretation. More generally, it places dreaming as a neurobiologically driven, functionally relevant phenomenon with significance for emotional regulation, memory consolidation, and clinical intervention in sleep medicine.
Sleep is an essential biological process regulating emotions and involving interactions between limbic and cortical systems. The amygdala, hippocampus, insula, and anterior cingulate cortex function as a bottom-up network for emotional processing, whereas prefrontal regions control the top-down aspect. Neurophysiological, neurochemical, and network modifications affect these circuits during sleep stages i.e. rapid eye movement (REM) and non-REM (NREM) phases. Sleep deprivation throws this balance out of synchrony, making emotional responsiveness increasingly pronounced and regulation more compromised, and can lead to, or aggravate, mental health disorders such as depression, anxiety, and PTSD. This chapter integrates fundamental neuroscience, system modelling, neuroimaging, and clinical insights into account of limbic and cortical brain circuitry in the context of sleep-induced emotional regulation.
Precision medicine is on the verge of transforming the treatment of neurodegenerative diseases (NDDs) like Alzheimer's disease (AD) and Parkinson's disease (PD), in response to the intricate interactions of genetic, epigenetic, environmental, and lifestyle factors underlying disease heterogeneity. As the world's aging populations grow, with dementia cases expected to double by 2040 and the costs amounting to over €130 billion a year in Europe alone, there is an urgent need for novel strategies to stem the socioeconomic costs of NDDs. Conventional "one-drug-fits-all" strategies that depend on late-stage symptom treatment are progressively insufficient for disorders that are marked by heterogeneous molecular pathways and unpredictable clinical courses. Recent improvements in artificial intelligence (AI), multi-omics integration, and biomarker research now allow patients to be stratified into subpopulations following their genetic risk profiles, neuroimaging signatures, and fluid biomarkers (e.g., amyloid-beta, tau, α-synuclein), enabling early diagnosis and focused treatments. For example, artificial intelligence platforms such as the IHI-PROMINENT project are creating forecasting algorithms to chart disease progression and tailor treatment outcomes, and gene therapy and antisense oligonucleotides (ASOs) address precise mutations in familial AD and PD. These advances are supported by pharmacogenomics, which individualizes drug regimens according to metabolic profiles to reduce side effects and maximize efficacy. Still, translating these advances into practice has major barriers to overcome, such as large-scale biomarker validation, multi-omics standardization, and incorporating real-world evidence from digital health technologies. Aging populations only add complexity to this environment, as comorbidities like diabetes and cardiovascular diseases interact with neurodegenerative pathways, requiring system-based, holistic approaches to care. Equity is still a key challenge: differences in access to sophisticated diagnostics (e.g., PET scans, CSF examination) and expensive therapies (e.g., monoclonal antibodies, CAR-T cell therapy) threaten to worsen global health disparities. In retaliation, initiatives such as the JPND research paradigm advance remote clinical trials and telemedicine platforms for the diverse community in decentralized settings, and policies target reducing financial disincentives through risk-sharing strategies and public-private partnerships. Precision medicine in the treatment of NDDs depends on an integrated network among academia, clinics, and industry, by taking advantage of communal biobanks and AI-enabled big data analysis, for refining the drug development process and validating new targets, e.g., neuroinflammatory signaling and gut-brain axis dysfunction. Innovations, like CRISPR-mediated editing and ambient neuroimaging, have innate or potential power to personalize treatment by identifying early-stage and even pre-symptomatic patients and modulating one's lifestyle in light of genetic risk. However ethical considerations around data privacy, algorithmic bias, and informed consent for Sustained therapeutic interventions over a lifetime should guide, not lag, the transformation. With the drive toward preventive rather than delayed care, precision medicine represents a revolutionary paradigm shift in health care, and a possibility to convert NDDs from devastatingly fatal diagnoses to easily managed chronic diseases and render equitable access to innovations possible for the masses. Success will require consistent investment in translational studies, interdisciplinary training, and global regulatory harmonization to translate the promise of precision medicine into tangible improvements in the quality of life for the millions of individuals afflicted with neurodegenerative disorders.
Pharmacogenomics and CRISPR-based treatments are two areas of precision medicine that are advancing together. Pharmacogenomics involves studying how differences in someone's genes can change the effect of medications on them. Pharmacogenomics helps reduce adverse reactions to drugs and improve healing by choosing and measuring drugs according to a patient's genetic information. Additionally, CRISPR-Cas systems now serve as leading genome editing tools that allow precise alterations at given points of the genome. CRISPR technology's use in pharmacogenomics creates new opportunities for modifying gene expression, fixing harmful mutations, and creating innovative treatment approaches. A more proactive approach to illness treatment is supported by this synergy, in which genetic factors serve as both direct targets for intervention and a basis for medication selection. This chapter examines the theoretical and practical frameworks that link CRISPR-based treatments with pharmacogenomics, emphasizing recent uses in pharmacoresistance, cancer, and monogenic diseases. To guarantee safe and fair deployment, it also covers the ethical, legal, and technical issues that need to be resolved. When combined, these technologies hold the potential to revolutionize medicine by facilitating individualized and curative drugs.
Neurodegenerative disorders (NDs), such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), present increasing issues associated with the unavoidable aging of the world's population. These challenges are further highlighted by the socioeconomic consequences of these diseases. The identification and use of biomarkers for prompt diagnosis, careful observation, and efficient treatment approaches is essential to overcoming these obstacles. The primary methods for diagnosing neurodegenerative illnesses are invasive procedures like lumbar punctures to measure CSF fluid or functional brain imaging methods. Biomarkers for underlying proteinopathy in blood serum and cerebral fluid have been the focus of recent biological research, particularly in vivo. With their ability to provide novel pathways for early detection, illness progression tracking, and individualized treatment plans, biomarkers have become essential instruments in precision medicine. The classification of biomarkers including fluid, digital imaging, and molecular biomarkers is examined in this chapter, with an emphasis on their function in neurodegenerative diseases. In neurodegenerative illnesses and the aging brain, tau, amyloid-β, α-synuclein, and TDP-43 are commonly seen to be deposited together rather than separately. These may be disregarded, and it might be challenging to determine their clinicopathological significance. An overview of illness pathophysiology, diagnostic implications, and the most recent molecular and ultrastructural categories for neurodegenerative disorders are given in this chapter. Addressing these issues through interdisciplinary research and technological advancements will be crucial for the future of biomarker-driven precision medicine. This chapter provides an in-depth overview of the evolving landscape of biomarkers and their transformative impact on the early detection and personalized treatment of neurodegenerative diseases.
Neurodegenerative diseases, marked by complex molecular mechanisms and diverse clinical features, challenge conventional research approaches. This chapter emphasizes the value of multi-omics integration in understanding the biology of Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis (ALS). Genomic studies reveal risk variants such as APOE ε4 in Alzheimer's and rare mutations in familial forms. Transcriptomics highlights gene expression changes, including synaptic dysfunction in early Parkinson's and alternative splicing errors in TARDBP-related ALS. Proteomics identifies key protein aggregates like amyloid beta and alpha-synuclein, along with modifications such as hyperphosphorylated tau that correlate with cognitive decline. Metabolomics uncovers metabolic alterations, including mitochondrial dysfunction in Parkinson's and lipid peroxidation in ALS, which contribute to disease progression. By combining these layers with high-throughput tools like single-cell sequencing, spatial transcriptomics, and mass spectrometry, researchers can reconstruct molecular networks linking genetic risk, gene regulation, protein dysfunction, and metabolic imbalance. This approach enables patient stratification into molecular subtypes, such as neuroinflammatory clusters defined by microglial gene signatures and cytokine expression. Biomarkers from blood and cerebrospinal fluid allow for minimally invasive disease monitoring. Despite challenges such as data heterogeneity and limited standardization, multi-omics approaches support biomarker discovery and therapeutic development. Integrating these datasets with neuroimaging and digital tools enhances diagnostic precision and guides targeted interventions, such as antisense therapies for SOD1-linked ALS. Multi-omics integration is thus a critical foundation for advancing personalized strategies in neurodegenerative disease research.
Parkinson's disease is a progressive neurodegenerative disorder characterized by dopaminergic neuron loss, α-synuclein aggregation, and oxidative stress-induced mitochondrial dysfunction. These contribute to motor symptoms like bradykinesia, tremor, rigidity, and postural instability, alongside non-motor issues such as cognitive decline and autonomic dysfunction. With rising global prevalence, especially in aging populations, understanding PD's complex pathophysiology is essential for advancing effective therapeutic strategies. This chapter offers a comprehensive overview of Parkinson's disease, covering its demographic trends, molecular mechanisms, symptoms, and current pharmacological treatments, including their mechanisms, classifications, benefits, and side effects. The central focus is on the emerging shift from standard symptomatic care to personalized, precision-based treatment strategies. These include gene-targeted therapies like RNA interference and CRISPR-Cas9, mitochondrial-based approaches, and stem cell innovations such as induced pluripotent stem cells (iPSCs). Pharmacogenetic profiling and biomarker-based interventions further support individualized therapeutic plans by predicting drug responses and tracking disease progression. This chapter explores patient-centric models like personalized neurorehabilitation, DBS, and cognitive-behavioral therapy, alongside advanced technologies such as AI, VR, AR, and wearable devices that enhance treatment, symptom monitoring, and real-time feedback through mobile applications. Lifestyle adaptations, including precision nutrition and customized exercise regimens, are highlighted as key components of comprehensive care. This chapter highlights a personalized, multi-dimensional approach to Parkinson's disease management, integrating molecular, technological, and lifestyle strategies to both ease symptoms and modify disease progression.
Healthcare is undergoing a transformation owing to artificial intelligence in medical diagnostics, which is providing unprecedented levels of efficiency and precision. AI is revolutionizing the detection, analysis, and treatment of diseases, especially in the field of medical diagnostics. AI can process enormous volumes of data quickly and effectively by utilizing machine learning and deep learning algorithms, giving healthcare professionals important insights. In addition to improving diagnosis accuracy, these developments are opening the door to early identification and individualized treatment regimens. AI transforms the interpretation of MRIs, computed tomography scans, and X-rays with extreme precision, outperforming humans in the identification of cancerous and vision-related ailments. Early disease identification has been greatly enhanced by the application of AI to identify anomalies associated with neurological and cardiac disorders. By facilitating the rapid identification of cardiac conditions and cancer, predictive analytics helps to significantly lower the number of false positives and negatives. AI reduces errors and human exhaustion and bias, resulting in more equitable and reliable provision of healthcare. With an emphasis on its uses, advantages, and the difficulties associated with incorporating these state-of-the-art technologies into clinical practice, this chapter examines the different ways artificial intelligence is changing medical diagnostics.
Advances in neuroimaging are revolutionizing the landscape of precision neurology by enabling high-resolution, multimodal visualization of brain structure, function, and pathology. As traditional, symptom-based frameworks fall short in capturing the biological complexity of neurodegenerative diseases, imaging modalities such as structural MRI, diffusion tensor imaging, functional MRI, PET, and hybrid PET/MRI have emerged as essential tools for early diagnosis, patient stratification, and therapeutic monitoring. These technologies not only reveal hallmark features like hippocampal atrophy and disrupted neural networks but also uncover molecular signatures such as amyloid and tau deposition, synaptic density, and neuroinflammation. Integration with artificial intelligence (AI) and machine learning (ML) further enhances diagnostic precision by decoding subtle imaging patterns, facilitating subtype classification, and predicting disease progression. Despite transformative progress, disparities in access and implementation remain a critical challenge, particularly in low- and middle-income countries. This chapter provides a comprehensive overview of neuroimaging modalities, their diagnostic and prognostic relevance across major neurodegenerative conditions including Alzheimer's disease, Parkinson's disease, ALS, and frontotemporal dementia and the evolving role of hybrid platforms and AI integration in shaping the future of individualized neurological care.
Neurodegenerative illnesses such as Alzheimer's disease, Parkinson's disease, and Huntington's disease present with increasing neurodegeneration and derangement. Genomic mutations and epigenetic changes are known to be part of their causation. This particular chapter discusses the key genetic factors like APP, SNCA, HTT, and C9orf72 mutations that influence the development and course of the disease. Furthermore, we examine epigenetic mechanisms-DNA methylation, histone modification, and non-coding RNAs-that regulate gene expression and contribute to neuronal susceptibility. The discussion also focuses on environmental and behavioral factors that affect the epigenome, highlighting gene-environment interactions. Advancements in omics technology and integrative studies have enhanced comprehension of biological pathways and uncovered novel biomarkers and therapeutic targets. The chapter therefore integrates epigenetic and genetic viewpoints to elucidate the intricate regulatory processes responsible for neurodegeneration and highlights potential avenues for early diagnosis, precision medicine, and therapeutic interventions aimed at remodeling disease pathways.
