Neuronal network dysfunction represents a fundamental pathological feature of neurodegenerative diseases, including Alzheimer's Disease (AD), Parkinson's Disease (PD), and related disorders1. Rather than affecting neurons uniformly, neurodegenerative processes disrupt specific neuronal networks, leading to characteristic patterns of functional impairment that precede cell death2. This page explores the mechanisms underlying network dysfunction, the relationship between protein pathology and network failure, and emerging therapeutic approaches targeting circuit-level dysfunction. [1]
The concept of "network degeneration" proposes that pathogenic proteins propagate along anatomically connected neural pathways, explaining the characteristic spatial patterns of pathology observed in different diseases3. Understanding these network-level changes provides insights into disease progression and offers novel therapeutic targets that may be more accessible than attempting to rescue individual neurons. [2]
Synaptic loss represents the strongest correlate of cognitive impairment in neurodegenerative diseases4. The earliest functional changes include: [3]
Presynaptic alterations: [4]
Postsynaptic changes: [5]
Microglia-mediated synaptic pruning becomes pathological in neurodegeneration: [6]
Energy failure at synapses precedes overall neuronal death: [7]
Gamma rhythm abnormalities are a hallmark of neurodegenerative conditions: [8]
Alzheimer's Disease: [9]
Parkinson's Disease: [10]
Interneuron dysfunction: [11]
Network-level changes: [12]
Gamma entrainment: [13]
The DMN, active during rest and memory consolidation, shows early dysfunction in AD: [14]
Connectivity reductions: [15]
Structural correlates: [16]
The salience network, involved in attention and behavior switching, shows characteristic changes: [17]
Basal ganglia-thalamocortical circuits: [18]
Compensation and maladaptation: [19]
EEG provides direct measures of network dysfunction: [20]
Quantitative EEG markers: [21]
Event-related potentials: [22]
MEG offers excellent spatial resolution for network analysis: [23]
Direct cortical recordings in patients provide unparalleled detail: [24]
Aβ disrupts network function through multiple mechanisms: [25]
Acute synaptic toxicity: [26]
Network-level spread: [27]
Tau demonstrates prion-like spread through neural networks: [28]
Anatomical propagation: [29]
Functional consequences: [30]
α-Synuclein pathology follows specific network patterns: [31]
Prion-like propagation: [32]
Network effects: [33]
-早期 synaptic dysfunction before inclusions form [34]
DBS exemplifies circuit-targeting therapy: [35]
Parkinson's Disease: [36]
Emerging applications: [37]
Non-invasive network modulation shows promise: [38]
AD: Repeated TMS sessions improve cognitive function [39]
PD: [40]
Mild electrical current modulates network activity: [41]
Calcium channels: [42]
Sodium channels: [43]
Potassium channels: [44]
Glutamate: [45]
GABA:
Acetylcholine:
Microglia and astrocytes contribute to network dysfunction:
Microglial network effects:
Astrocyte dysfunction:
APP/PS1 mice:
α-Synuclein transgenic mice:
Networks attempt to compensate for pathology:
Higher baseline function provides resilience:
Limited endogenous repair mechanisms exist:
Neurophysiological endpoints:
Behavioral measures:
Combining network measures with other biomarkers:
The characteristic network dysfunction in AD follows a predictable pattern:
Early stage (preclinical):
Mild cognitive impairment:
Moderate-severe AD:
Network-based biomarkers:
PD demonstrates distinctive motor and non-motor network signatures:
Motor network abnormalities:
Non-motor network changes:
Cortical-subcortical interactions:
ALS network dysfunction reveals system-specific vulnerabilities:
Motor network:
Cognitive networks:
HD shows characteristic basal ganglia network disruption:
Motor circuit:
Cognitive circuits:
Resting-state functional connectivity has become standard:
Analysis approaches:
Strengths:
Limitations:
Static connectivity underestimates network complexity:
Dynamic connectivity approaches:
Applications in neurodegeneration:
Understanding network dysfunction informs drug development:
Current approaches:
Emerging strategies:
Individual network profiles may guide treatment:
Network-based stratification:
Network biomarkers:
Neuronal network dysfunction represents a core feature of neurodegenerative diseases that bridges molecular pathology and clinical symptoms. Understanding network-level changes provides mechanistic insights, identifies biomarkers, and reveals therapeutic targets. While current interventions remain limited, circuit-targeted approaches including deep brain stimulation, transcranial stimulation, and novel neuromodulation techniques offer promise for preserving function in neurodegenerative diseases.
The recognition that neurodegeneration occurs at the network level rather than affecting neurons uniformly has fundamentally changed our understanding of disease progression. Network-based biomarkers now complement traditional pathological assessments, offering the potential for earlier diagnosis and more sensitive tracking of disease progression. Furthermore, circuit-targeted therapies provide a new therapeutic avenue that bypasses some of the limitations of molecular-targeted approaches.
Future directions include the development of closed-loop neuromodulation systems that respond dynamically to pathological network activity, the integration of network-level biomarkers into clinical trial design, and the application of precision medicine approaches that tailor interventions based on individual network profiles. As our understanding of network dysfunction deepens, we can anticipate more effective strategies for preserving brain function in the face of neurodegenerative pathology.
Iaccarino HF, Singer AC, Martorell AJ, et al. Gamma frequency entrainment attenuates amyloid burden. Nature. 2016. ↩︎
Brown P. Oscillatory nature of human basal ganglia activity. Exp Brain Res. 2003. ↩︎
Limousin P, Pollak P. Deep brain stimulation of the subthalamic nucleus in Parkinson's disease. Neuropsychopharmacology. 2022. ↩︎
Hu H, Gan J, Jonas P. 'Interneurons: fast-spiking parvalbumin-expressing interneurons'. Curr Opin Neurobiol. 2014. ↩︎
Mann EO, Paulsen O. Role of GABAergic inhibition in hippocampal network oscillations. Trends Neurosci. 2007. ↩︎
Adaikkan C, Tysnes OB, Haugen VB, et al. Gamma entrainment for Alzheimer's disease. J Alzheimers Dis. 2022. ↩︎
Benussi A, Cantoni V, Cotelli MS, et al. Transcranial stimulation for Alzheimer's disease. J Alzheimers Dis. 2022. ↩︎
Greicius MD, Srivastava G, Reiss AL, et al. Default-mode activity in aging. Proc Natl Acad Sci USA. 2004. ↩︎
Adams JN, Maass A, Harrison TM, et al. Cortical tau relates to connectivity in aging. Cerebral Cortex. 2019. ↩︎
Zhou J, Seeley WW. Network dysfunction in Alzheimer's disease and frontotemporal dementia. Lancet Neurol. 2014. ↩︎
Hammond C, Bergman H, Brown P. Pathological synchronization in Parkinson's disease. Trends Neurosci. 2007. ↩︎
Peterson DS, Horak FB. Neural control of walking in Parkinson's disease. Handb Clin Neurol. 2022. ↩︎
Dauwels J, Vialatte F, Cichocki A. Diagnosis of Alzheimer's disease from EEG. Curr Alzheimer Res. 2010. ↩︎
Polich J. 'Updating P300: an integrative theory of P3a and P3b'. Clin Neurophysiol. 2007. ↩︎
Stam CJ. Modern network science of neurological disorders. Nat Rev Neurosci. 2014. ↩︎
Jacobs J, Zijlmans M, Chatton B, et al. Intracranial EEG for epilepsy. Lancet Neurol. 2020. ↩︎
Demuro A, Mina E, Kayed R, et al. Calcium dysregulation and membrane disruption as a neurotoxic mechanism of Aβ. J Biol Chem. 2015. ↩︎
Bero AW, Yan P, Roh JH, et al. Neuronal activity regulates the spreading of Aβ. Nat Neurosci. 2011. ↩︎
Braak H, Del Tredici K. Are there tau-initiated spreading mechanisms in AD? Acta Neuropathol. Acta Neuropathol. 2017. ↩︎
Spires-Jones TL, Hyman BT. The intersection of amyloid and tau in AD. Annu Rev Neurosci. 2014. ↩︎
Goedert M, Masuda-Suzukake M, Avila J, et al. Prion-like propagation of tau aggregates. Brain. 2017. ↩︎
Luk KC, Kehm VM, Lee VM. Intracerebral inoculation of pathological α-synuclein. J Neurosci. 2012. ↩︎
Benabid AL, Pollak P, Louveau A, et al. Combined (thalamotomy and stimulation) stereotactic surgery of the VIM nucleus. Appl Neurophysiol. 1987. ↩︎
Hamani C, Lozano AM. Memory enhancement in Alzheimer's disease. Nat Rev Neurol. 2022. ↩︎
Lefaucheur JP, Aleman A, Baeken C, et al. Evidence-based guidelines for TMS. Clin Neurophysiol. 2020. ↩︎
Fregni F, Simon DK, Wu A, et al. Non-invasive brain stimulation for Parkinson's disease. J Neurol Neurosurg Psychiatry. 2020. ↩︎
Hill AT, Zoghi M, Bhattacharya A, et al. Transcranial direct current stimulation for memory. Neurosci Biobehav Rev. 2019. ↩︎
Bezprozvanny I. Calcium signaling and neurodegenerative diseases. Cell Calcium. 2009. ↩︎
Spruston N. 'Pyramidal neurons: dendritic structure'. Scholarpedia. 2008. ↩︎
Shalomov B, Yehezkiel M, Goldberg EM. Potassium channelopathies of neuronal excitability. Adv Exp Med Biol. 2021. ↩︎
Traynelis SF, Wollmuth LP, McBain CJ, et al. Glutamate receptor ion channels. Pharmacol Rev. 2010. ↩︎
Palop JJ, Mucke L. Synaptic depression and excitatory imbalance. Nat Neurosci. 2010. ↩︎
Mufson EJ, Mahady L, Counts S, et al. Basal forebrain cholinergic dysfunction in AD. Nat Rev Neurol. 2022. ↩︎
Wake H, Moorhouse AJ, Nabekura J. Microglia and synaptic plasticity. Neuroscientist. 2011. ↩︎
Verkhratsky A, Nedergaard M. Physiology of astroglia. Physiol Rev. 2018. ↩︎
Gulisano W, Melone M, Li Puma DD, et al. EEG power spectrum in APP/PS1 mice. J Alzheimers Dis. 2019. ↩︎
Yun SP, Shin TH, Baek H, et al. α-Synuclein and network dysfunction in mouse models. Neurobiol Aging. 2021. ↩︎
de la Rosa K, Tretter E, Huber A. Electrophysiology in transgenic mouse models. Methods Mol Biol. 2019. ↩︎
Tampellini D. 'Synaptic activity and Alzheimer''s disease: a critical crosstalk'. J Alzheimers Dis. 2020. ↩︎
Stern Y. Cognitive reserve in aging and AD. Lancet Neurol. 2012. ↩︎
Boldrini M, Fulmore CA, Tartt AN, et al. Human hippocampal neurogenesis. Cell Stem Cell. 2018. ↩︎
Babiloni C, Del Percio C, Triggiani AI, et al. Clinical trials with neurophysiological biomarkers. J Alzheimers Dis. 2021. ↩︎
Schendan HE, Searleman A, Tyson's S. Cognitive assessment in neurodegeneration. Handb Clin Neurol. 2022. ↩︎
Jack CR, Knopman DS, Jagust WJ, et al. Update on hypothetical AD biomarkers. Lancet Neurol. 2013. ↩︎
Gilron R, Little S, Perrone R, et al. Long-term wireless streaming of neural recordings. Nat Biomed Eng. 2021. ↩︎