Late-life depression (LLD) remains a major clinical challenge due to the lack of robust neurobiological markers, limiting the development of targeted and effective treatments.
Methods:
Here, we introduce a novel computational framework that applies the Ising model to multimodal neuroimaging data, inferring excitation–inhibition balance (EIB) by integrating structural and functional connectivity. In a large cohort from the Alzheimer’s Disease Neuroimaging Initiative (113 LLD subjects, 165 controls, all aged 65+), we identified group-level differences in EIB, independent of common neurodegenerative risk factors including apolipoprotein E and amyloid burden.
Results:
We identified increased whole-brain EIB in LLD (p = 0.0014, β = 0.039), independent of common neurodegenerative risk factors. After multiple comparisons correction, network-level analysis revealed pronounced EIB dysregulation in the limbic (p < 0.001), dorsal attention (p < 0.001), and basal ganglia networks (p = 0.007). At the regional level, significant alterations emerged in the hippocampus, thalamus, and anterior cingulate cortex (all p < 0.05)—areas central to emotional regulation, executive function, and cognitive processing. Notably, mild cognitive impairment displayed the opposite pattern with decreased whole-brain EIB (p = 0.042, β = −0.023), providing a clear neurobiological distinction between depression and neurodegeneration.
Conclusions:
These findings establish EIB dysregulation as a core mechanism in LLD, offering a novel explanatory framework for why traditional antidepressants often fail in this population. The regional specificity of these alterations suggests new approaches for circuit-based interventions, particularly relevant to emerging rapid-acting antidepressants that modulate the EIB. This mechanistic framework offers a biomarker for diagnosis and treatment monitoring and informs novel, circuit-based therapeutic strategies.
GunningFM, OberlinLE, SchierM, et al.Brain-based mechanisms of late-life depression: Implications for novel interventions. Semin Cell Dev Biol, 2021; 116:169–179; doi: 10.1016/j.semcdb.2021.05.002
3.
JellingerKA. The heterogeneity of late-life depression and its pathobiology: A brain network dysfunction disorder. J Neural Transm (Vienna), 2023; 130(8):1057–1076; doi: 10.1007/s00702-023-02648-z
4.
GlassmanAH. Depression and cardiovascular comorbidity. Dialogues Clin Neurosci, 2007; 9(1):9–17.
5.
InvernizziS, Simoes LoureiroI, Kandana ArachchigeKG, et al.Late-Life depression, cognitive impairment, and relationship with Alzheimer’s disease. Dement Geriatr Cogn Disord, 2021; 50(5):414–424; doi: 10.1159/000519453
6.
DinizBS, ButtersMA, AlbertSM, et al.Late-life depression and risk of vascular dementia and Alzheimer’s disease: Systematic review and meta-analysis of community-based cohort studies. Br J Psychiatry, 2013; 202(5):329–335; doi: 10.1192/bjp.bp.112.118307
7.
LyM, KarimHT, BeckerJT, et al.Late-life depression and increased risk of dementia: A longitudinal cohort study. Transl Psychiatry, 2021; 11(1):147; doi: 10.1038/s41398-021-01269-y
8.
SzymkowiczSM, GerlachAR, HomiackD, et al.Biological factors influencing depression in later life: Role of aging processes and treatment implications. Transl Psychiatry, 2023; 13(1):160; doi: 10.1038/s41398-023-02464-9
9.
Nautiyal KM, Hen R. Serotonin receptors in depression: from A to B. F1000Res 2017;6:123; doi: 10.12688/f1000research.9736.1
10.
MeltzerCC, SmithG, DeKoskyST, et al.Serotonin in aging, late-life depression, and Alzheimer’s disease: The emerging role of functional imaging. Neuropsychopharmacology, 1998; 18(6):407–430; doi: 10.1016/S0893-133X(97)00194-2
11.
BrenderR, MulsantBH, BlumbergerDM. An update on antidepressant pharmacotherapy in late-life depression. Expert Opin Pharmacother, 2021; 22(14):1909–1917; doi: 10.1080/14656566.2021.1921736
12.
KimY-K, HanK-M. Neural substrates for late-life depression: A selective review of structural neuroimaging studies. Prog Neuropsychopharmacol Biol Psychiatry, 2021; 104:110010; doi: 10.1016/j.pnpbp.2020.110010
13.
LinC, HuangC-M, FanY-T, et al.Cognitive reserve moderates effects of white matter hyperintensity on depressive symptoms and cognitive function in late-life depression. Front Psychiatry, 2020; 11:249; doi: 10.3389/fpsyt.2020.00249
14.
AlexopoulosGS. Mechanisms and treatment of late-life depression. Transl Psychiatry, 2019; 9(1):188; doi: 10.1038/s41398-019-0514-6
15.
ScangosKW, StateMW, MillerAH, et al.New and emerging approaches to treat psychiatric disorders. Nat Med, 2023; 29(2):317–333; doi: 10.1038/s41591-022-02197-0
16.
KarimHT, ReynoldsCF, SmagulaSF. Chapter 27 - Neuroimaging Biomarkers of Late-Life Major Depressive Disorder Pathophysiology, Pathogenesis, and Treatment Response. In: Personalized Psychiatry. (BauneBT. ed) Academic Press: San Diego; 2020; pp. 339–356; doi: 10.1016/B978-0-12-813176-3.00027-4
17.
VeceraCM, CourtesC, JonesA, et al.Pharmacotherapies targeting GABA-Glutamate neurotransmission for treatment-resistant depression. Pharmaceuticals (Basel), 2023; 16(11):1572; doi: 10.3390/ph16111572
18.
ChakiS, WatanabeM. Antidepressants in the post-ketamine Era: Pharmacological approaches targeting the glutamatergic system. Neuropharmacology, 2023; 223:109348; doi: 10.1016/j.neuropharm.2022.109348
19.
KrystalJH, KayeAP, JeffersonS, et al.Ketamine and the neurobiology of depression: Toward next-generation rapid-acting antidepressant treatments. Proc Natl Acad Sci U S A, 2023; 120(49):e2305772120; doi: 10.1073/pnas.2305772120
20.
DecoG, Ponce-AlvarezA, HagmannP, et al.How local excitation–inhibition ratio impacts the whole brain dynamics. J Neurosci, 2014; 34(23):7886–7898; doi: 10.1523/JNEUROSCI.5068-13.2014
21.
HuY-T, TanZ-L, HirjakD, et al.Brain-wide changes in excitation-inhibition balance of major depressive disorder: A systematic review of topographic patterns of GABA- and glutamatergic alterations. Mol Psychiatry, 2023; 28(8):3257–3266; doi: 10.1038/s41380-023-02193-x
22.
PericaMI, CalabroFJ, LarsenB, et al.Development of frontal GABA and glutamate supports excitation/inhibition balance from adolescence into adulthood. Prog Neurobiol, 2022; 219:102370; doi: 10.1016/j.pneurobio.2022.102370
23.
SearsSM, HewettSJ. Influence of glutamate and GABA transport on brain excitatory/inhibitory balance. Exp Biol Med (Maywood), 2021; 246(9):1069–1083; doi: 10.1177/1535370221989263
24.
KirischukS. Keeping excitation–inhibition ratio in balance. Int J Mol Sci, 2022; 23(10):5746; doi: 10.3390/ijms23105746
25.
BruiningH, HardstoneR, Juarez-MartinezEL, et al.Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics. Sci Rep, 2020; 10(1):9195; doi: 10.1038/s41598-020-65500-4
CepedaC, OikonomouKD, CummingsD, et al.Developmental origins of cortical hyperexcitability in Huntington’s disease: Review and new observations. J Neurosci Res, 2019; 97(12):1624–1635; doi: 10.1002/jnr.24503
28.
CurotJ, BarbeauE, DespouyE, et al.Local neuronal excitation and global inhibition during epileptic fast ripples in humans. Brain, 2023; 146(2):561–575; doi: 10.1093/brain/awac319
29.
Foss-FeigJH, AdkinsonBD, JiJL, et al.Searching for cross-diagnostic convergence: Neural mechanisms governing excitation and inhibition balance in Schizophrenia and autism spectrum disorders. Biol Psychiatry, 2017; 81(10):848–861; doi: 10.1016/j.biopsych.2017.03.005
30.
GodfreyKEM, GardnerAC, KwonS, et al.Differences in excitatory and inhibitory neurotransmitter levels between depressed patients and healthy controls: A systematic review and meta-analysis. J Psychiatr Res, 2018; 105:33–44; doi: 10.1016/j.jpsychires.2018.08.015
31.
FogaçaMV, DumanRS. Cortical GABAergic dysfunction in stress and depression: New insights for therapeutic interventions. Front Cell Neurosci, 2019; 13:87; doi: 10.3389/fncel.2019.00087
32.
SeltenM, van BokhovenH, Nadif KasriN. Inhibitory control of the excitatory/inhibitory balance in psychiatric disorders. F1000Res, 2018; 7:23; doi: 10.12688/f1000research.12155.1
33.
SteenlandK, KarnesC, SealsR, et al.Late-Life depression as a risk factor for mild cognitive impairment or Alzheimer’s disease in 30 US Alzheimer’s disease centers. J Alzheimers Dis, 2012; 31(2):265–275; doi: 10.3233/JAD-2012-111922
34.
JengJ-S, LiC-T, LinH-C, et al.Antidepressant-resistant depression is characterized by reduced short- and long-interval cortical inhibition. Psychol Med, 2020; 50(8):1285–1291; doi: 10.1017/S0033291719001223
35.
MaestúF, de HaanW, BuscheMA, et al.Neuronal excitation/inhibition imbalance: Core element of a translational perspective on Alzheimer pathophysiology. Ageing Res Rev, 2021; 69:101372; doi: 10.1016/j.arr.2021.101372
36.
HopfieldJJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A, 1982; 79(8):2554–2558.
37.
PerettoP. Collective properties of neural networks: A statistical physics approach. Biol Cybern, 1984; 50(1):51–62; doi: 10.1007/BF00317939
38.
FortelI, ButlerM, KorthauerLE, et al.Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function. Netw Neurosci, 2022; 6(2):420–444; doi: 10.1162/netn_a_00220
39.
FortelI, ButlerM, KorthauerLE, et al.Brain dynamics through the lens of statistical mechanics by unifying structure and function. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. (ShenD, LiuT, PetersTM eds). Lecture Notes in Computer Science Springer International Publishing: Cham; 2019; pp. 503–511; doi: 10.1007/978-3-030-32254-0_56
40.
FortelI, ZhanL, AjiloreO, et al.Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimer’s disease. J Alzheimers Dis, 2023; 95(4):1449–1467; doi: 10.1101/2023.08.21.554061
41.
FortelI, KorthauerLE, MorrisseyZ, et al.Connectome signatures of hyperexcitation in cognitively intact middle-aged female APOE-ε4 carriers. Cereb Cortex, 2020; 30(12):6350–6362; doi: 10.1093/cercor/bhaa190
42.
DumanRS, SanacoraG, KrystalJH. Altered connectivity in depression: GABA and glutamate neurotransmitter deficits and reversal by novel treatments. Neuron, 2019; 102(1):75–90; doi: 10.1016/j.neuron.2019.03.013
43.
YinY-Y, WangY-H, LiuW-G, et al.The role of the excitation: Inhibition functional balance in the mPFC in the onset of antidepressants. Neuropharmacology, 2021; 191:108573; doi: 10.1016/j.neuropharm.2021.108573
44.
DhamiP, QuiltyLC, SchwartzmannB, et al.Response inhibition and predicting response to pharmacological and cognitive behavioral therapy treatments for major depressive disorder: A Canadian biomarker integration network for depression study. Biol Psychiatry Cogn Neurosci Neuroimaging, 2023; 8(2):162–170; doi: 10.1016/j.bpsc.2021.12.012
45.
VoineskosD, BlumbergerDM, ZomorrodiR, et al.Altered transcranial magnetic stimulation–electroencephalographic markers of inhibition and excitation in the dorsolateral prefrontal cortex in major depressive disorder. Biol Psychiatry, 2019; 85(6):477–486; doi: 10.1016/j.biopsych.2018.09.032
46.
AustS, BrakemeierE-L, SpiesJ, et al.Efficacy of augmentation of cognitive behavioral therapy with transcranial direct current stimulation for depression: A randomized clinical trial. JAMA Psychiatry, 2022; 79(6):528–537; doi: 10.1001/jamapsychiatry.2022.0696
MackinRS, InselPS, LandauS, et al.; Alzheimer’s Disease Neuroimaging Initiative and the ADNI Depression Project. Late-Life depression is associated with reduced cortical amyloid burden: Findings from the Alzheimer’s disease neuroimaging initiative depression project. Biol Psychiatry, 2021; 89(8):757–765; doi: 10.1016/j.biopsych.2020.06.017
49.
RhodesE, InselPS, ButtersMA, et al.; ADNI Depression Project. The impact of amyloid burden and APOE on rates of cognitive impairment in late life depression. J Alzheimers Dis, 2021; 80(3):991–1002; doi: 10.3233/JAD-201089
50.
HamiltonM. A rating scale for depression. J Neurol Neurosurg Psychiatry, 1960; 23(1):56–62; doi: 10.1136/jnnp.23.1.56
51.
American Psychiatric Association, American Psychiatric Association. (eds). Diagnostic and Statistical Manual of Mental Disorders: DSM-5. 5th ed.. American Psychiatric Association: Washington, D.C; 2013.
52.
LeeWH, FrangouS. Linking functional connectivity and dynamic properties of resting-state networks. Sci Rep, 2017; 7(1):16610; doi: 10.1038/s41598-017-16789-1
53.
RoudiY, TyrchaJ, HertzJ. Ising model for neural data: Model quality and approximate methods for extracting functional connectivity. Phys Rev E Stat Nonlin Soft Matter Phys, 2009; 79(5 Pt 1):051915; doi: 10.1103/PhysRevE.79.051915
54.
NghiemT-A, MarreO, DestexheA, et al.Pairwise Ising Model Analysis of Human Cortical Neuron Recordings. In: Geometric Science of Information. (NielsenF, BarbarescoF eds). Lecture Notes in Computer Science Springer International Publishing: Cham; 2017; pp. 257–264; doi: 10.1007/978-3-319-68445-1_30
55.
JagustWJ, LandauSM, KoeppeRA, et al.The Alzheimer’s disease neuroimaging initiative 2 PET core: 2015. Alzheimers Dement, 2015; 11(7):757–771; doi: 10.1016/j.jalz.2015.05.001
56.
SarawagiA, SoniND, PatelAB. Glutamate and GABA homeostasis and neurometabolism in major depressive disorder. Front Psychiatry, 2021; 12:637863; doi: 10.3389/fpsyt.2021.637863
57.
LissemoreJI, BhandariA, MulsantBH, et al.Reduced GABAergic cortical inhibition in aging and depression. Neuropsychopharmacology, 2018; 43(11):2277–2284; doi: 10.1038/s41386-018-0093-x
58.
TonioloS, SenA, HusainM. Modulation of brain hyperexcitability: Potential new therapeutic approaches in Alzheimer’s disease. Int J Mol Sci, 2020; 21(23):9318; doi: 10.3390/ijms21239318
59.
van NifterickAM, MulderD, DuineveldDJ, et al.Resting-state oscillations reveal disturbed excitation–inhibition ratio in Alzheimer’s disease patients. Sci Rep, 2023; 13(1):7419; doi: 10.1038/s41598-023-33973-8
60.
ScadutoP, LauterbornJC, CoxCD, et al.Functional excitatory to inhibitory synaptic imbalance and loss of cognitive performance in people with Alzheimer’s disease neuropathologic change. Acta Neuropathol, 2023; 145(3):303–324; doi: 10.1007/s00401-022-02526-0
61.
SpellmanT, ListonC. Toward circuit mechanisms of pathophysiology in depression. Am J Psychiatry, 2020; 177(5):381–390; doi: 10.1176/appi.ajp.2020.20030280
62.
TarttAN, MarianiMB, HenR, et al.Dysregulation of adult hippocampal neuroplasticity in major depression: Pathogenesis and therapeutic implications. Mol Psychiatry, 2022; 27(6):2689–2699; doi: 10.1038/s41380-022-01520-y
63.
MaybergHS. Modulating dysfunctional limbic-cortical circuits in depression: Towards development of brain-based algorithms for diagnosis and optimised treatment. Br Med Bull, 2003; 65(1):193–207; doi: 10.1093/bmb/65.1.193
64.
ThaseME. Depression and sleep: Pathophysiology and treatment. Dialogues Clin Neurosci, 2006; 8(2):217–226.
65.
NuttD. Anxiety and depression: Individual entities or two sides of the same coin? Int J Psychiatry Clin Pract, 2004; 8(Suppl 1):19–24; doi: 10.1080/13651500410005513
66.
NolanM, RomanE, NasaA, et al.Hippocampal and amygdalar volume changes in major depressive disorder: A targeted review and focus on stress. Chronic Stress (Thousand Oaks), 2020; 4; doi: 10.1177/2470547020944553
67.
LinnemannC, LangUE. Pathways connecting late-life depression and dementia. Front Pharmacol, 2020; 11:279; doi: 10.3389/fphar.2020.00279
68.
BergerT, LeeH, YoungAH, et al.Adult hippocampal neurogenesis in major depressive disorder and Alzheimer’s disease. Trends Mol Med, 2020; 26(9):803–818; doi: 10.1016/j.molmed.2020.03.010
69.
KhundakarAA, ThomasAJ. Cellular morphometry in late-life depression: A review of postmortem studies. Am J Geriatr Psychiatry, 2014; 22(2):122–132; doi: 10.1016/j.jagp.2013.06.003
70.
Rashidi-RanjbarN, MirandaD, ButtersMA, et al.Evidence for structural and functional alterations of frontal-executive and corticolimbic circuits in late-life depression and relationship to mild cognitive impairment and dementia: A systematic review. Front Neurosci, 2020; 14:253; doi: 10.3389/fnins.2020.00253
71.
BobbDS, AdinoffB, LakenSJ, et al.Neural correlates of successful response inhibition in unmedicated patients with late-life depression. Am J Geriatr Psychiatry, 2012; 20(12):1057–1069; doi: 10.1097/JGP.0b013e318235b728
72.
AizensteinHJ, ButtersMA, WuM, et al.Altered functioning of the executive control circuit in late-life depression: Episodic and persistent phenomena. Am J Geriatr Psychiatry, 2009; 17(1):30–42; doi: 10.1097/JGP.0b013e31817b60af
73.
Calvo-Flores GuzmánB, VinnakotaC, GovindpaniK, et al.The GABAergic system as a therapeutic target for Alzheimer’s disease. J Neurochem, 2018; 146(6):649–669; doi: 10.1111/jnc.14345
74.
McGrathT, BaskervilleR, RogeroM, et al.Emerging evidence for the widespread role of glutamatergic dysfunction in neuropsychiatric diseases. Nutrients, 2022; 14(5):917; doi: 10.3390/nu14050917
75.
FrisardiV, PanzaF, FarooquiAA. Late-life depression and Alzheimer’s disease: The glutamatergic system inside of this mirror relationship. Brain Res Rev, 2011; 67(1–2):344–355; doi: 10.1016/j.brainresrev.2011.04.003
76.
TaylorWD, ZaldDH, FelgerJC, et al.Influences of dopaminergic system dysfunction on late-life depression. Mol Psychiatry, 2022; 27(1):180–191; doi: 10.1038/s41380-021-01265-0
77.
BennettMR. The prefrontal–limbic network in depression: Modulation by hypothalamus, basal ganglia and midbrain. Prog Neurobiol, 2011; 93(4):468–487; doi: 10.1016/j.pneurobio.2011.01.006
78.
FigielGS, NemeroffCB. Magnetic Resonance Imaging of the Basal Ganglia in Depression. Limbic Motor Circuits and Neuropsychiatry CRC Press; 1993.
79.
LuscherB, FengM, JeffersonSJ. Chapter Two - Antidepressant Mechanisms of Ketamine: Focus on GABAergic Inhibition. In: Advances in Pharmacology. (RSDuman, JHKrystal. eds). Rapid Acting Antidepressants Academic Press; 2020; pp. 43–78; doi: 10.1016/bs.apha.2020.03.002
80.
WeckmannK, DeeryMJ, HowardJA, et al.Ketamine’s effects on the glutamatergic and GABAergic systems: A proteomics and metabolomics study in Mice. Mol Neuropsychiatry, 2019; 5(1):42–51; doi: 10.1159/000493425
81.
KoushY, RothmanDL, BeharKL, et al.Human brain functional MRS reveals interplay of metabolites implicated in neurotransmission and neuroenergetics. J Cereb Blood Flow Metab, 2022; 42(6):911–934; doi: 10.1177/0271678X221076570
82.
LiG, HsuL-M, WuY, et al.Excitation-Inhibition imbalance in Alzheimer’s disease using multiscale neural model inversion of resting-state fMRI. Commun Med (Lond), 2023:2022; doi: 10.1101/2022.10.04.22280681
83.
GuH, HuY, ChenX, et al.Regional excitation-inhibition balance predicts default-mode network deactivation via functional connectivity. Neuroimage, 2019; 185:388–397; doi: 10.1016/j.neuroimage.2018.10.055
84.
JavittDC. Glutamate as a therapeutic target in psychiatric disorders. Mol Psychiatry, 2004; 9(11):984–997, 979; doi: 10.1038/sj.mp.4001551
85.
MarsmanA, van den HeuvelMP, KlompDWJ, et al.Glutamate in schizophrenia: A focused review and meta-analysis of 1H-MRS studies. Schizophr Bull, 2013; 39(1):120–129; doi: 10.1093/schbul/sbr069
86.
BenesFM, BerrettaS. GABAergic interneurons: Implications for understanding schizophrenia and bipolar disorder. Neuropsychopharmacology, 2001; 25(1):1–27; doi: 10.1016/S0893-133X(01)00225-1
87.
BłaszczykJW. Parkinson’s disease and neurodegeneration: GABA-Collapse hypothesis. Front Neurosci, 2016; 10:269; doi: 10.3389/fnins.2016.00269
88.
GhatakS, TalantovaM, McKercherSR, et al.Novel therapeutic approach for excitatory/inhibitory imbalance in neurodevelopmental and neurodegenerative diseases. Annu Rev Pharmacol Toxicol, 2021; 61:701–721; doi: 10.1146/annurev-pharmtox-032320-015420
89.
LopatinaOL, MalinovskayaNA, KomlevaYK, et al.Excitation/inhibition imbalance and impaired neurogenesis in neurodevelopmental and neurodegenerative disorders. Rev Neurosci, 2019; 30(8):807–820; doi: 10.1515/revneuro-2019-0014
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