Network abnormalities and interneuron dysfunction in Alzheimer disease

Jorge J Palop, Lennart Mucke, Jorge J Palop, Lennart Mucke

Abstract

The function of neural circuits and networks can be controlled, in part, by modulating the synchrony of their components' activities. Network hypersynchrony and altered oscillatory rhythmic activity may contribute to cognitive abnormalities in Alzheimer disease (AD). In this condition, network activities that support cognition are altered decades before clinical disease onset, and these alterations predict future pathology and brain atrophy. Although the precise causes and pathophysiological consequences of these network alterations remain to be defined, interneuron dysfunction and network abnormalities have emerged as potential mechanisms of cognitive dysfunction in AD and related disorders. Here, we explore the concept that modulating these mechanisms may help to improve brain function in these conditions.

Figures

Figure 1 |. Synchrony and functional states…
Figure 1 |. Synchrony and functional states of networks.
The degree of correlated neuronal activity (synchrony) reflects the functional state of networks and circuits. a | To illustrate this relationship at the network level, the top panels show local field potentials (LFPs) recorded from four electrodes (1–4) inserted 1 mm apart into the cat parietal cortex (suprasylvian gyrus) during sleep (left) and wakefulness (right). Network activity during resting and non-active states is predominantly characterized by synchronized slow-frequency and high-amplitude fluctuations (red shading). By contrast, network activity during active states is characterized by desynchronized fast-frequency and low-amplitude fluctuations (blue shading). The bottom panels show hypothetical representations of the amplitude (red line) and frequency (blue line) of LFP fluctuations and of the associated network synchrony (pink line). b | Membrane potential recordings of two layer 2/3 (L2/3) pyramidal neurons from mouse parietal cortex (whisker barrel cortex) during resting and active (whisker use) periods illustrate that neurons desynchronize during active periods. Part a is republished with permission of Society for Neuroscience, from Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states, Destexhe, A., Contreras, D. & Steriade, M., 19, 11, 1999; permission conveyed through Copyright Clearance Center Inc. Part b is from REF., Nature Publishing Group.
Figure 2 |. Encoding reveals network dysfunction…
Figure 2 |. Encoding reveals network dysfunction in people with mild cognitive impairment.
Memory encoding causes specific changes in brain activity-related functional MRI (fMRI) signals across different networks. People at increased risk for Alzheimer disease (AD) show early changes in the activation and deactivation of specific networks. a | Lateral (left panel) and medial (right panel) views of a healthy left hemibrain. During encoding in healthy people, network activity increases in task-related networks (blue) and decreases in non-task-related networks (yellow/orange). Anti-correlated activity (blue versus yellow/orange) in these two widely distributed and non-overlapping networks is also evident when spontaneous fluctuations of fMRI signals during resting states are examined (not shown). The default mode network (yellow and orange regions) includes brain regions that show decreased fMRI activity during attention-demanding tasks but become active during inwardly oriented mental activity. b | In healthy individuals whose brain activity was monitored by fMRI during a cognitive task, a relatively smaller extent of hippocampal activation and a relatively greater extent of precuneal deactivation were associated with better cognitive performance. c | Pattern separation and completion are cognitive functions that heavily rely on the hippocampal formation and allow us to discriminate (pattern separation) or merge (pattern completion) similar representations or episodes. In a pattern-separation task, in which individuals were asked to discriminate between slightly different trowels (left panel), patients with amnestic mild cognitive impairment (MCI) made more pattern-completion errors (that is, they failed to discriminate slightly different trowels) and had aberrant hyperactivation of the dentate gyrus (DG) and CA3 regions of the hippocampus on fMRI (right panel). Treatment with the antiepileptic drug levetiracetam reversed the hippocampal hyperactivation and improved the patients’ ability to discriminate between images that were similar but not identical. d | During a face–name association task, patients with MCI and amyloid deposits in the brain (as revealed by a Pittsburg compound B-positive (PiB+) signal on positron emission tomography) and patients with AD showed deactivation deficits in the precuneus. Part a is adapted from REF. . Part b is adapted with permission from REF. , Springer. Part c is adapted with permission from REF. , Cell Press/Elsevier. Part d is adapted with permission from REF. , Cell Press/Elsevier.
Figure 3 |. Neuronal activity regulates amyloid-β…
Figure 3 |. Neuronal activity regulates amyloid-β production and deposition.
a | Most patients with Alzheimer disease (AD) and some people without dementia have increased Pittsburg compound B-positive amyloid deposits in the brain (red). Amyloid deposits predominate in brain regions of the default mode network (blue), which shows deactivation deficits in AD (FIG. 2), suggesting a potential link between aberrant neuronal activity and amyloid deposition. b | In APP-A7 mice, chronic, optogenetic stimulation of pyramidal cells in the entorhinal cortex triggered epileptiform activity and increased amyloid deposition in the molecular layer of the dentate gyrus, directly supporting the notion that aberrant neuronal activity can promote amyloid deposition in vivo. c | At the synaptic level, an increase in the frequency of action potentials (APs) proportionally enhances amyloid-β1–42 (Aβ1–42) and amyloid-β1–40 (Aβ1–40) production. Compared with regular firing, burst firing reduces the Aβ1–42/Aβ1–40 ratio. d | Basal neurotransmitter release determines the ‘filter’ mode of synapses and regulates synaptic plasticity. The top panel indicates that excitatory synapses with lower release probability (high-pass-filter synapses) have greater presynaptic Ca2+ build-up, produce lower Aβ1–42/Aβ1–40 ratios, exhibit synaptic facilitation and primarily transfer potentiated responses. The bottom panel depicts synapses with higher release probability (low-pass-filter synapses), which have less presynaptic Ca2+ build-up, produce higher Aβ1–42/Aβ1–40 ratios and show synaptic depression as well as enhanced spike transfer. In AD, synapses may shift from high- to low-pass synaptic filtering. e | Electroencephalograhy recordings capture the combined electrical output of neuronal ensembles. In such recordings, most familial AD (FAD) mice, including hAPP-J20 (REF. 96), APP/PSEN1dE9 (REFS 102,103), Tg2576 (REF. 104), 5xFAD, 3xTg-AD, APP/TTA-EC, APP/TTA-CaMKIIα, and APP23 (REF. 107) mice (Supplementary information S1 (table)), show intermittent large-amplitude epileptiform discharges (denoted by the asterisk in hAPP-J20 mice; bottom panel), which provide evidence of network hypersynchrony. fMRI, functional MRI; PET, positron emission tomography. Part a is republished with permission of Society for Neuroscience, from Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory, Buckner, R. L. et al., 25, 34, 2005; permission conveyed through Copyright Clearance Center, Inc. Part e is adapted with permission from REF. , Cell Press/Elsevier.
Figure 4 |. Neuronal ensembles generate oscillatory…
Figure 4 |. Neuronal ensembles generate oscillatory activity patterns.
a | Oscillatory rhythms (grey boxes) emerge from, and control, the activity of neuronal ensembles that are formed by excitatory neurons (blue) and inhibitory interneurons (red). The amplitude, or power, of low-frequency oscillations increases during rest, whereas the amplitude of high-frequency oscillations increases during activity. Action potentials (APs) of excitatory neurons are phase-locked to high-power, lower-frequency oscillations (top grey box), whereas APs of inhibitory cells are phase-locked to high-power, high-frequency oscillations (bottom grey box). b | The left panel shows local field potential (LFP) gamma oscillations (blue) and APs (red) for a parvalbumin-positive (PV+) cell in area CA3, revealing a close association between APs and the phase of gamma oscillations. In the right panel, the AP firing rate (colour coded) is shown as a function of gamma phase for the same PV+ cell, which prominently fires during the ascending phase of gamma oscillations. c | Optogenetic stimulation of PV+ and pyramidal cells at different frequencies resulted in a cell type- and frequency-specific generation of oscillatory activity. 40-Hz stimulation of PV+, but not pyramidal, cells increased gamma power, whereas 8-Hz stimulation of pyramidal, but not PV+, cells increased theta power. d | In macaques, visual stimulation (15s of dim 1 Hz illumination) increased the power of LFP gamma (30–150 Hz) oscillations in a task-related network (visual area V3; left panel) and decreased LFP power across multiple oscillatory frequencies in a default mode network region (posterior cingulate cortex; right panel). SEM, standard error of the mean. Part b is from REF., Nature Publishing Group. Part c is from REF. , Nature Publishing Group. Part d is adapted from Bentley, W. J., Li, J.M., Snyder, A. Z., Raichle, M.E. & Snyder, L.H., Oxygen level and LFP in task-positive and task-negative areas: bridging BOLD fMRI and electrophysiology, Cerebral Cortex, 2014, 26, 1, 346–357, by permission of Oxford Journals.
Figure 5 |. Close association between behavioural…
Figure 5 |. Close association between behavioural state, gamma oscillations and epileptiform activity in mice and humans.
a,b | Behavioural activity, full-frequency range spectrograms, gamma oscillatory power, and distribution of epileptic discharges in cortical networks of an hAPP-J20 mouse (part a) and a human with epilepsy (part b). In mice, exploration (active) of a novel environment robustly increased gamma oscillatory power and reduced epileptiform discharges, suggesting that the brain state modulates brain rhythms and network hypersynchrony. In humans, successful, but not unsuccessful, memory encoding also increased gamma oscillatory power and reduced epileptiform discharges. c | In our hypothetical model, memory encoding requires frequency-specific modulation of oscillatory frequencies, which reduces network hypersynchrony. Part a is adapted with permission from REF. , Cell Press/Elsevier. Part b is adapted from Matsumoto, J.Y. et al., Network oscillations modulate interictal epileptiform spike rate during human memory, Brain, 2013, 136, 8, 2444–2456, by permission of Oxford Journals.
Figure 6 |. Targeting interneurons to improve…
Figure 6 |. Targeting interneurons to improve Alzheimer disease-related network dysfunction.
a | In hAPP-J20 mice (middle panel), reduced levels of the voltage-gated sodium chanel subunit Nav1.1 in parvalbumin-positive (PV+) cells were associated with reduced gamma power, epileptiform activity and cognitive impairment (the level of cognitive performance is denoted by the plus symbols). Restoring Nav1.1 levels in PV+ cells with an Nav1.1-BAC (bacterial artificial chromosome) transgene (right panel) reduced all of these deficits. b | APOE4-KI mice have increased neuronal levels of phosphorylated tau, age-dependent loss of somatostatin-positive interneurons in the hilus of the dentate gyrus, and seizures. Memory deficits in these mice were reduced by pentobarbital treatment, tau removal and transplantation of interneuron precursor cells. Part a is adapted with permission from REF. , Cell Press/Elsevier.

References

    1. Seidman LJ et al. Medial temporal lobe default mode functioning and hippocampal structure as vulnerability indicators for schizophrenia: A MRI study of non-psychotic adolescent first-degree relatives. Schizophr. Res 159, 426–434 (2014).
    1. Fahoum F, Zelmann R, Tyvaert L, Dubeau F. & Gotman J. Epileptic discharges affect the default mode network--fMRI and intracerebral EEG evidence. PLoS One 8, e68038 (2013).
    1. Oser N. et al. Default mode network alterations during language task performance in children with benign epilepsy with centrotemporal spikes (BECTS). Epilepsy Behav. 33, 12–17 (2014).

    1. Sperling RA et al. Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 63, 178–188 (2009).

      This study shows that PiB+ amyloid deposits are associated with deactivation deficits in a default network component in non-demented individuals and patients with MCI, linking aberrant neuronal activity and pathological changes.

    1. Anticevic A. et al. The role of default network deactivation in cognition and disease. Trends Cogn. Sci 16, 584–592 (2012).
    1. Filippini N. et al. Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. Proc. Natl. Acad. Sci. USA 106, 7209–7214 (2009).
    1. Mondadori CR et al. Enhanced brain activity may precede the diagnosis of Alzheimer’s disease by 30 years. Brain 129, 2908–2922 (2006).

    1. Bateman RJ et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med 367, 795–804 (2012).

      This study demonstrates changes in CSF biomarkers, cerebral amyloid deposition, and brain metabolism in carriers of autosomal dominant FAD mutations at least two decades before the estimated onset of clinical symptoms.

    1. Reiman EM et al. Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: A case-control study. Lancet. Neurol 11, 1048–1056 (2012).

      This study shows hippocampal hyperactivity and deactivation deficits in the DMN in 18–26-year-old asymptomatic PSEN1 mutation carriers.

    1. Jasper HH Cortical excitatory state and variability in human brain rhythms. Science 83, 259–260 (1936).
    1. Destexhe A, Contreras D. & Steriade M. Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states. J. Neurosci 19, 4595–4608 (1999).
    1. Poulet JF & Petersen CC Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature 454, 881–885 (2008).
    1. Poulet JF, Fernandez LM, Crochet S. & Petersen CC Thalamic control of cortical states. Nat. Neurosci 15, 370–372 (2012).
    1. Karran E, Mercken M. & De Strooper B. The amyloid cascade hypothesis for Alzheimer’s disease: An appraisal for the development of therapeutics. Nat. Rev. Drug Discov 10, 698–712 (2011).
    1. Benilova I, Karran E. & De Strooper B. The toxic Aβ oligomer and Alzheimer’s disease: An emperor in need of clothes. Nat. Neurosci 15, 349–357 (2012).
    1. Karch CM, Cruchaga C. & Goate AM Alzheimer’s disease genetics: from the bench to the clinic. Neuron 83, 11–26 (2014).
    1. Gomez-Isla T. et al. The impact of different presenilin 1 andpresenilin 2 mutations on amyloid deposition, neurofibrillary changes and neuronal loss in the familial Alzheimer’s disease brain: Evidence for other phenotype-modifying factors. Brain. 122, 1709–1719 (1999).
    1. Jankowsky JL et al. Mutant presenilins specifically elevate the levels of the 42 residue beta-amyloid peptide in vivo: evidence for augmentation of a 42-specific γ _secretase. Hum. Mol. Genet 13, 159–170 (2004).
    1. Steiner H. & Haass C. Intramembrane proteolysis by presenilins. Nat. Rev. Mol. Cell Biol 1, 217–224 (2000).
    1. Tsubuki S, Takaki Y. & Saido TC Dutch, Flemish, Italian, and Arctic mutations of APP and resistance of Aβ to physiologically relevant proteolytic degradation. Lancet 361, 1957–1958 (2003).
    1. Nilsberth C. et al. The ‘Arctic’ APP mutation (E693G) causes Alzheimer’s disease by enhanced Aβ protofibril formation. Nat. Neurosci 4, 887–893 (2001).
    1. Shimada H. et al. Clinical course of patients with familial early-onset Alzheimer’s disease potentially lacking senile plaques bearing the E693 Δ mutation in amyloid precursor protein. Dement. Geriatr. Cogn. Disord 32, 45–54 (2011).
    1. Jonsson T. et al. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature 488, 96–99 (2012).
    1. Maloney JA et al. Molecular mechanisms of Alzheimer disease protection by the A673T allele of amyloid precursor protein. J. Biol. Chem 289 30990–31000, (2014).

    1. Li S. et al. Soluble oligomers of amyloid β-protein facilitate hippocampal long-term depression by disrupting neuronal glutamate uptake. Neuron 62, 788–801 (2009).

      This study shows that amyloid-β blocks neuronal glutamate uptake at synapses, which results in glutamate spillover, aberrant activation of extra- or perisynaptic GluN2B-containing NMDARs, and enhanced long-term depression.

    1. Shankar GM et al. Amyloid-β protein dimers isolated directly from Alzheimer’s brains impair synaptic plasticity and memory. Nat. Med 14, 837–842 (2008).
    1. Li S. et al. Soluble Aβ oligomers inhibit long-term potentiation through a mechanism involving excessive activation of extrasynaptic NR2B-containing NMDA receptors. J. Neurosci 31, 6627–6638 (2011).
    1. Cleary JP et al. Natural oligomers of the amyloid-β protein specifically disrupt cognitive function. Nat. Neurosci 8, 79–84 (2005).
    1. Musiek ES & Holtzman DM Three dimensions of the amyloid hypothesis: time, space and ‘wingmen’. Nat. Neurosci 18, 800–806 (2015).
    1. Liu P. et al. Quaternary structure defines a large class of amyloid-β oligomers neutralized by sequestration. Cell Rep. 11, 1760–1771 (2015).
    1. Ashe KH & Zahs KR Probing the biology of Alzheimer’s disease in mice. Neuron 66, 631–645 (2010).
    1. Morris M, Maeda S, Vossel K. & Mucke L. The many faces of tau. Neuron 70, 410–426 (2011).
    1. Huang Y. & Mucke L. Alzheimer mechanisms and therapeutic strategies. Cell 148, 1204–1222 (2012).
    1. Heppner FL, Ransohoff RM & Becher B. Immune attack: the role of inflammation in Alzheimer disease. Nat. Rev. Neurosci 16, 358–372 (2015).
    1. Maeda S. et al. Expression of A152T human tau causes age-dependent neuronal dysfunction and loss in transgenic mice. EMBO Rep. 17, 530–551 (2016).
    1. SantaCruz K. et al. Tau suppression in a neurodegenerative mouse model improves memory function. Science 309, 476–481 (2005).
    1. Mandelkow EM & Mandelkow E. Biochemistry and cell biology of tau protein in neurofibrillary degeneration. Cold Spring Harb. Perspect. Med 2, a006247 (2012).
    1. Min SW et al. Critical role of acetylation in tau-mediated neurodegeneration and cognitive deficits. Nat. Med 21, 1154–1162 (2015).
    1. Mahley RW & Huang Y. Apolipoprotein E sets the stage: response to injury triggers neuropathology. Neuron 76, 871–885 (2012).
    1. Pike KE et al. β-Amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer’s disease. Brain 130, 2837–2844 (2007).
    1. Savva GM et al. Age, neuropathology, and dementia. N. Engl. J. Med 360, 2302–2309 (2009).
    1. Ingelsson M. et al. Early Aβ accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology 62, 925–931 (2004).
    1. Lesne SE et al. Brain amyloid-β oligomers in ageing and Alzheimer’s disease. Brain 136, 1383–1398 (2013).
    1. Selkoe DJ Soluble oligomers of the amyloid β-protein impair synaptic plasticity and behavior. Behav. Brain Res 192, 106–113 (2008).
    1. Klein WL, Krafft GA & Finch CE Targeting small Aβ oligomers: The solution to an Alzheimer’s disease conundrum. Trends Neurosci. 24, 219–224 (2001).
    1. Walsh DM & Selkoe DJ Aβ oligomers - a decade of discovery. J. Neurochem 101, 1172–1184 (2007).
    1. Shankar GM et al. Natural oligomers of the Alzheimer amyloid-β protein induce reversible synapse loss by modulating an NMDA-type glutamate receptor-dependent signaling pathway. J. Neurosci 27, 2866–2875 (2007).
    1. Cheng IH et al. Accelerating amyloid- β fibrillization reduces oligomer levels and functional deficits in Alzheimer disease mouse models. J. Biol. Chem 282, 23818–23828 (2007).
    1. Tomiyama T. et al. A mouse model of amyloid β oligomers: their contribution to synaptic alteration, abnormal tau phosphorylation, glial activation, and neuronal loss in vivo. J. Neurosci 30, 4845–4856 (2010).
    1. Lesné S. et al. A specific amyloid-β protein assembly in the brain impairs memory. Nature 440, 352–357 (2006).
    1. Meilandt WJ et al. Neprilysin overexpression inhibits plaque formation but fails to reduce pathogenic Aβ oligomers and associated cognitive deficits in human amyloid precursor protein transgenic mice. J. Neurosci 29, 1977–1986 (2009).
    1. Mably AJ et al. Anti- Aβ antibodies incapable of reducing cerebral Aβ oligomers fail to attenuate spatial reference memory deficits in J20 mice. Neurobiol. Dis 82, 372–384 (2015).
    1. De Strooper B. Loss-of-function presenilin mutations in Alzheimer disease. EMBO Rep. 8, 141–146 (2007).
    1. Pimplikar SW Neuroinflammation in Alzheimer’s disease: from pathogenesis to a therapeutic target. J. Clin. Immunol 34, S64–S69 (2014).
    1. Xia D. et al. Presenilin-1 knockin mice reveal loss-of-function mechanism for familial Alzheimer’s disease. Neuron 85, 967–981 (2015).
    1. Bredesen DE Neurodegeneration in Alzheimer’s disease: caspases and synaptic element interdependence. Mol. Neurodegener 4, 27 (2009).
    1. Bentley WJ, Li JM, Snyder AZ, Raichle ME & Snyder LH Oxygen level and LFP in task-positive and task-negative areas: bridging BOLD fMRI and electrophysiology. Cereb. Cortex, 26, 346–357 (2014).
    1. Boyatzis RE, Rochford K. & Jack AI Antagonistic neural networks underlying differentiated leadership roles. Front. Hum. Neurosci 8, 114 (2014).
    1. Raichle ME et al. A default mode of brain function. Proc. Natl. Acad. Sci. USA 98, 676–682 (2001).
    1. Greicius MD, Krasnow B, Reiss AL & Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. USA 100, 253–258 (2003).
    1. Sperling RA et al. Functional alterations in memory networks in early Alzheimer’s disease. Neuromolecular Med. 12, 27–43 (2010).

    1. Bookheimer SY et al. Patterns of brain activation in people at risk for Alzheimer’s disease. N. Engl. J. Med 343, 450–456 (2000).

      This study shows hippocampal hyperactivity during memory tasks in asymptomatic APOE ε4 carriers.

    1. Trivedi MA et al. fMRI activation during episodic encoding and metacognitive appraisal across the lifespan: risk factors for Alzheimer’s disease. Neuropsychologia 46, 1667–1678 (2008).
    1. Kunz L. et al. Reduced grid-cell-like representations in adults at genetic risk for Alzheimer’s disease. Science 350, 430–433 (2015).
    1. Quiroz YT et al. Hippocampal hyperactivation in presymptomatic familial Alzheimer’s disease. Ann. Neurol 68, 865–875 (2010).
    1. Sepulveda-Falla D, Glatzel M. & Lopera F. Phenotypic profile of early-onset familial Alzheimer’s disease caused by presenilin-1 E280A mutation. J. Alzheimers Dis 32, 1–12 (2012).

    1. Bakker A. et al. Reduction of hippocampal hyperactivity improves cognition in amnestic mild cognitive impairment. Neuron 74, 467–474 (2012).

      This study shows that treatment with low doses of the antiepileptic drug levetiracetam reduces hippocampal hyperactivity and improves cognition in patients with MCI.

    1. Dickerson BC et al. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology 65, 404–411 (2005).
    1. Celone KA et al. Alterations in memory networks in mild cognitive impairment and Alzheimer’s disease: an independent component analysis. J. Neurosci 26, 10222–10231 (2006).
    1. Persson J. et al. Altered deactivation in individuals with genetic risk for Alzheimer’s disease. Neuropsychologia 46, 1679–1687 (2008).
    1. Dickerson BC et al. Medial temporal lobe function and structure in mild cognitive impairment. Ann. Neurol 56, 27–35 (2004).
    1. Putcha D. et al. Hippocampal hyperactivation associated with cortical thinning in Alzheimer’s disease signature regions in non-demented elderly adults. J. Neurosci 31, 17680–17688 (2011).
    1. Bakker A, Albert MS, Krauss G, Speck CL & Gallagher M. Response of the medial temporal lobe network in amnestic mild cognitive impairment to therapeutic intervention asessed by fMRI and memory task performance. NeuroImage Clin. 7, 688–698 (2015).

    1. Sanchez PE et al. Levetiracetam suppresses neuronal network dysfunction and reverses synaptic and cognitive deficits in an Alzheimer’s disease model. Proc. Natl. Acad. Sci. USA 109, E2895–E2903 (2012).

      This study shows that levetiracetam reduces network hypersynchrony, synaptic deficits and behavioral abnormalities in hAPP-J20 mice.

    1. Nygaard HB et al. Brivaracetam, but not ethosuximide, reverses memory impairments in an Alzheimer’s disease mouse model. Alzheimer Res. Ther 7, 25 (2015).
    1. Suberbielle E. et al. Physiologic brain activity causes DNA double-strand breaks in neurons, with exacerbation by amyloid-β. Nat. Neurosci 16, 613–621 (2013).
    1. Suberbielle E. et al. DNA repair factor BRCA1 depletion occurs in Alzheimer brains and impairs cognitive function in mice. Nat. Commun 6, 8897 (2015).
    1. Madabhushi R, Pan L. & Tsai LH DNA damage and its links to neurodegeneration. Neuron 83, 266–282 (2014).
    1. Kim J, Basak JM & Holtzman DM The role of apolipoprotein E in Alzheimer’s disease. Neuron 63, 287–303 (2009).
    1. Saito T. et al. Single App knock-in mouse models of Alzheimer’s disease. Nat. Neurosci 17, 661–663 (2014).
    1. Roh JH et al. Disruption of the sleep-wake cycle and diurnal fluctuation of β-amyloid in mice with Alzheimer’s disease pathology. Sci. Transl. Med 4, 150ra122 (2012).
    1. Kang JE et al. Amyloid- β dynamics are regulated by orexin and the sleep-wake cycle. Science 326, 1005–1007 (2009).
    1. Xie L. et al. Sleep drives metabolite clearance from the adult brain. Science 342, 373–377 (2013).
    1. Bero AW et al. Neuronal activity regulates the regional vulnerability to amyloid- β deposition. Nat. Neurosci 14, 750–756 (2011).
    1. Yamamoto K. et al. Chronic optogenetic activation augments Aβ pathology in a mouse model of Alzheimer disease. Cell Rep. 11, 859–865 (2015).

    1. Dolev I. et al. Spike bursts increase amyloid- β 40/42 ratio by inducing a presenilin-1 conformational change. Nat. Neurosci 16, 587–595 (2013).

      This study shows that amyloid-β production and the amyloid-β1–42/amyloid-β1–40 ratio are regulated by neuronal action potential firing through calcium-dependent conformational changes in PSEN1 at presynaptic terminals.

    1. Nitsch RM, Farber SA, Growdon HJ & Wurtman RJ Release of amyloid β-protein precursor derivatives by electrical depolarization of rat hippocampal slices. Proc. Natl. Acad. Sci. USA 90, 5191–5193 (1993).

    1. Kamenetz F. et al. APP processing and synaptic function. Neuron 37, 925–937 (2003).

      This study shows that neuronal activity regulates amyloid-β production.

    1. Abbott LF & Regehr WG Synaptic computation. Nature 431, 796–803 (2004).
    1. Laviv T. et al. Basal GABA regulates GABABR conformation and release probability at single hippocampal synapses. Neuron 67, 253–267 (2010).
    1. Palop JJ & Mucke L. Amyloid-β-induced neuronal dysfunction in Alzheimer’s disease: from synapses toward neural networks. Nat. Neurosci 13, 812–818 (2010).
    1. Cirrito JR et al. Synaptic activity regulates interstitial fluid amyloid- β levels in vivo. Neuron 48, 913–922 (2005).
    1. Abramov E. et al. Amyloid-β as a positive endogenous regulator of release probability at hippocampal synapses. Nat. Neurosci 12, 1567–1576 (2009).
    1. Puzzo D. et al. Picomolar amyloid- β positively modulates synaptic plasticity and memory in hippocampus. J. Neurosci 28, 14537–14545 (2008).
    1. Willem M. et al. η-Secretase processing of APP inhibits neuronal activity in the hippocampus. Nature, 526, 443–447 (2015).

    1. Palop JJ et al. Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease. Neuron 55, 697–711 (2007).

      This study shows that hAPP-J20 mice have network hyperexcitability that is associated with extensive anatomical and physiological alterations in learning and memory centers.

    1. Yang XF, Weisenfeld A. & Rothman SM Prolonged exposure to levetiracetam reveals a presynaptic effect on neurotransmission. Epilepsia 48, 1861–1869 (2007).
    1. Meehan AL, Yang X, McAdams BD, Yuan L. & Rothman SM A new mechanism for antiepileptic drug action: vesicular entry may mediate the effects of levetiracetam. J. Neurophysiol 106, 1227–1239 (2011).

    1. Kurudenkandy FR et al. Amyloid-β-induced action potential desynchronization and degradation of hippocampal gamma oscillations is prevented by interference with peptide conformation change and aggregation. J. Neurosci 34, 11416–11425 (2014).

      This study shows that amyloid-β reduces gamma oscillations by desynchronizing the action potentials of hippocampal pyramidal cells.

    1. Sanchez-Mejia RO et al. Phospholipase A2 reduction ameliorates cognitve deficits in mouse model of Alzheimer’s disease. Nat. Neurosci 11, 1311–1318 (2008).

    1. Busche MA et al. Critical role of soluble amyloid-β for early hippocampal hyperactivity in a mouse model of Alzheimer’s disease. Proc. Natl. Acad. Sci. USA 109, 8740–8745 (2012).

      This study shows that amyloid-β increases hippocampal neuronal activity in vivo by calcium imaging.

    1. Minkeviciene R. et al. Amyloid β-induced neuronal hyperexcitability triggers progressive epilepsy. J. Neurosci 29, 3453–3462 (2009).
    1. Um JW et al. Alzheimer amyloid-β oligomer bound to postsynaptic prion protein activates Fyn to impair neurons. Nat. Neurosci 15, 1227–1235 (2012).
    1. Bezzina C. et al. Early onset of hypersynchronous network activity and expression of a marker of chronic seizures in the Tg2576 mouse model of Alzheimer’s disease. PLoS ONE 10, e0119910 (2015).
    1. Harris JA et al. Transsynaptic progression of amyloid-β-induced neuronal dysfunction within the entorhinal-hippocampal network. Neuron 68, 428–441 (2010).
    1. Born HA et al. Genetic suppression of transgenic APP rescues hypersynchronous network activity in a mouse model of Alzeimer’s disease. J. Neurosci 34, 3826–3840 (2014).
    1. Ittner AA, Gladbach A, Bertz J, Suh LS & Ittner LM p38 MAP kinase-mediated NMDA receptor-dependent suppression of hippocampal hypersynchronicity in a mouse model of Alzheimer inverted question marks disease. Acta Neuropathol. Commun 2, 149 (2014).
    1. Roberson ED et al. Amyloid-β/Fyn-induced synaptic, network, and cognitive impairments depend on tau levels in multiple mouse models of Alzheimer’s disease. J. Neurosci 31, 700–711 (2011).

    1. Busche MA et al. Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer’s disease. Science 321, 1686–1689 (2008).

      This study shows that APP23xPS45 mice have hyperactive neurons and synchronized neuronal activity by calcium imaging.

    1. Roberson ED et al. Reducing endogenous tau ameliorates amyloid β-induced deficits in an Alzheimer’s disease mouse model. Science 316, 750–754 (2007).

      This study shows that endogenous tau promotes APP-dependent network hypersynchrony and cognitive decline.

    1. Ittner LM et al. Dendritic function of tau mediates amyloid-β toxicity in Alzheimer’s disease mouse models. Cell 142, 387–397 (2010).
    1. Palop JJ et al. Vulnerability of dentate granule cells to disruption of Arc expression in human amyloid precursor protein transgenic mice. J. Neurosci 25, 9686–9693 (2005).
    1. Chin J. et al. Fyn kinase induces synaptic and cognitive impairments in a transgenic mouse model of Alzheimer’s disease. J. Neurosci 25, 9694–9703 (2005).
    1. Grienberger C. et al. Staged decline of neuronal function in vivo in an animal model of Alzheimer’s disease. Nat. Commun 3, 774 (2012).
    1. Rudinskiy N. et al. Orchestrated experience-driven Arc responses are disrupted in a mouse model of Alzheimer’s disease. Nat. Neurosci 15, 1422–1429 (2012).
    1. Spires TL & Hyman BT Neuronal structure is altered by amyloid plaques. Rev. Neurosci 15, 267–278 (2004).
    1. Koffie RM et al. Oligomeric amyloid β associates with postsynaptic densities and correlates with excitatory synapse loss near senile plaques. Proc. Natl. Acad. Sci. USA 106, 4012–4017 (2009).
    1. Siskova Z. et al. Dendritic structural degeneration is functionally linked to cellular hyperexcitability in a mouse model of Alzheimer’s disease. Neuron 84, 1023–1033 (2014).
    1. Garcia-Cabrero AM et al. Hyperexcitability and epileptic seizures in a model of frontotemporal dementia. Neurobiol. Dis 58, 200–208 (2013).
    1. Hunter JM et al. Emergence of a seizure phenotype in aged apolipoprotein epsilon 4 targeted replacement mice. Brain Res. 1467, 120–132 (2012).
    1. Morris M. et al. Network dysfunction in α-synuclein transgenic mice and human Lewy body dementia. Ann. Clin. Transl. Neurol 2, 1012–1028 (2015).
    1. Siwek ME et al. Altered theta oscillations and aberrant cortical excitatory activity in the 5XFAD model of Alzheimer’s disease. Neural Plast. 2015, 781731 (2015).
    1. Lalonde R, Dumont M, Staufenbiel M. & Strazielle C. Neurobehavioral characterization of APP23 transgenic mice with the SHIRPA primary screen. Behav. Brain Res 157, 91–98 (2005).
    1. Hsiao KK et al. Age-related CNS disorder and early death in transgenic FVB/N mice overexpressing Alzheimer amyloid precursor proteins. Neuron 15, 1203–1218 (1995).
    1. Vossel KA et al. Tau reduction prevents Aβ-induced impairments in axonal transport. Science 330, 198 (2010).
    1. Devos SL et al. Antisense reduction of tau in adult mice protects against seizures. J. Neurosci 33, 12887–12897 (2013).
    1. Romanelli MF, Morris JC, Ashkin K. & Coben LA Advanced Alzheimer’s disease is a risk factor for late-onset seizures. Arch. Neurol 47, 847–850 (1990).
    1. Hauser WA, Morris ML, Heston LL & Anderson VE Seizures and myoclonus in patients with Alzheimer’s disease. Neurology 36, 1226–1230 (1986).
    1. Irizarry MC et al. Incidence of new-onset seizures in mild to moderate Alzheimer disease. Arch. Neurol 69, 368–372 (2012).
    1. Risse SC et al. Myoclonus, seizures, and paratonia in Alzheimer disease. Alzheimer Dis. Assoc. Disord 4, 217–225 (1990).
    1. Horvath A, Szucs A, Barcs G, Noebels JL & Kamondi A. Epileptic seizures in Alzheimer disease: a review. Alzheimer Dis. Assoc. Disord 30, 186–192 (2016).
    1. Imfeld P, Bodmer M, Schuerch M, Jick SS & Meier CR Seizures in patients with Alzheimer’s disease or vascular dementia: a population-based nested case-control analysis. Epilepsia 54, 700–707 (2013).

    1. Amatniek JC et al. Incidence and predictors of seizures in patients with Alzheimer’s disease. Epilepsia 47, 867–872 (2006).

      This study shows that early onset of AD is a risk factor for seizures.

    1. Sherzai D, Losey T, Vega S. & Sherzai A. Seizures and dementia in the elderly: Nationwide Inpatient Sample 1999–2008. Epilepsy Behav. 36, 53–56 (2014).
    1. Scarmeas N. et al. Seizures in Alzheimer disease: who, when, and how common? Arch. Neurol 66, 992–997 (2009).

    1. Vossel KA et al. Seizures and epileptiform activity in the early stages of Alzheimer disease. JAMA Neurol. 70, 1158–1166 (2013).

      This study shows that epileptic activity in patients with MCI or early AD can occur early in the disease course and is frequently non-convulsive in nature.

    1. Hesdorffer DC, Hauser WA, Annegers JF, Kokmen E. & Rocca WA Dementia and adult-onset unprovoked seizures. Neurology 46, 727–730 (1996).
    1. Cretin B. et al. Epileptic prodromal Alzheimer’s disease, a retrospective study of 13 new cases: expanding the spectrum of Alzheimer’s disease to an epileptic variant? J. Alzheimers Dis 52, 1125–1133 (2016).
    1. Palop JJ & Mucke L. Epilepsy and cognitive impairments in Alzheimer disease. Arch. Neurol 66, 435–440 (2009).
    1. Noebels J. A perfect storm: converging paths of epilepsy and Alzheimer’s dementia intersect in the hippocampal formation. Epilepsia 52 (Suppl. 1), 39–46 (2011).
    1. Larner AJ Presenilin-1 mutation Alzheimer’s disease: a genetic epilepsy syndrome? Epilepsy Behav. 21, 20–22 (2011).

    1. Larner AJ & Doran M. Clinical phenotypic heterogeneity of Alzheimer’s disease associated with mutations of the presenilin-1 gene. J. Neurol 253, 139–158 (2006).

      This study shows that seizures are a clinical feature of many pedigrees of FAD patients carrying PSEN1 mutations.

    1. Jayadev S. et al. Alzheimer’s disease phenotypes and genotypes associated with mutations in presenilin 2. Brain 133, 1143–1154 (2010).

    1. Cabrejo L. et al. Phenotype associated with APP duplication in five families. Brain 129, 2966–2976 (2006).

      This study shows that seizures are a clinical feature of many FAD patients carrying wild-type APP duplications.

    1. McNaughton D. et al. Duplication of amyloid precursor protein (APP), but not prion protein (PRNP) gene is a significant cause of early onset dementia in a large UK series. Neurobiol. Aging 33, e13–21 (2012).
    1. Zarea A. et al. Seizures in dominantly inherited Alzheimer disease. Neurology 87, 912–919 (2016).
    1. Lai F. & Williams RS A prospective study of Alzheimer disease in Down syndrome. Arch. Neurol 46, 849–853 (1989).
    1. Snider BJ et al. Novel presenilin 1 mutation (S170F) causing Alzheimer disease with Lewy bodies in the third decade of life. Arch. Neurol 62, 1821–1830 (2005).
    1. Moehlmann T. et al. Presenilin-1 mutations of leucine 166 equally affect the generation of the Notch and APP intracellular domains independent of their effect on Aβ42 production. Proc. Natl Acad. Sci. USA 99, 8025–8030 (2002).
    1. Jensen O. & Colgin LL Cross-frequency coupling between neuronal oscillations. Trends Cognit. Sci 11, 267–269 (2007).
    1. Viney TJ et al. Network state–dependent inhibition of identified hippocampal CA3 axo–axonic cells in vivo. Nat. Neurosci 16, 1802–1811 (2013).
    1. Tukker JJ, Fuentealba P, Hartwich K, Somogyi P. & Klausberger T. Cell type-specific tuning of hippocampal interneuron firing during gamma oscillations in vivo. J. Neurosci 27, 8184–8189 (2007).
    1. Uhlhaas PJ et al. Neural synchrony in cortical networks: history, concept and current status. Front. Integr. Neurosci 3, 17 (2009).

    1. Matsumoto JY et al. Network oscillations modulate interictal epileptiform spike rate during human memory. Brain 136, 2444–2456 (2013).

      This study shows that memory-induced oscillatory changes, including increaed gamma power, reduce epileptiform discharges in humans with epilepsy.

    1. Sederberg PB et al. Gamma oscillations distinguish true from false memories. Psychol. Sci 18, 927–932 (2007).

    1. Sederberg PB et al. Hippocampal and neocortical gamma oscillations predict memory formation in humans. Cereb. Cortex 17, 1190–1196 (2007).

      This study shows that increases in cortical and hippocampal gamma oscillations during memory tasks predict successful encoding of new memories in humans.

    1. Jensen O, Kaiser J. & Lachaux JP Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci. 30, 317–324 (2007).
    1. Yamamoto J, Suh J, Takeuchi D. & Tonegawa S. Successful execution of working memory linked to synchronized high-frequency gamma oscillations. Cell 157, 845–857 (2014).
    1. Tallon-Baudry C, Bertrand O, Delpuech C. & Pernier J. Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. J. Neurosci 16, 4240–4249 (1996).
    1. Lachaux JP et al. The many faces of the gamma band response to complex visual stimuli. Neuroimage 25, 491–501 (2005).
    1. Harris KD & Thiele A. Cortical state and attention. Nat. Rev. Neurosci 12, 509–523 (2011).
    1. Crone NE, Miglioretti DL, Gordon B. & Lesser RP Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain. 121, 2301–2315 (1998).
    1. Burke JF et al. Synchronous and asynchronous theta and gamma activity during episodic memory formation. J. Neurosci 33, 292–304 (2013).
    1. Sun L. et al. Evidence for dysregulated high-frequency oscillations during sensory processing in medication-naive, first episode schizophrenia. Schizophr. Res 150, 519–525 (2013).
    1. Herrmann CS & Demiralp T. Human EEG gamma oscillations in neuropsychiatric disorders. Clin. Neurophysiol 116, 2719–2733 (2005).
    1. Niessing J. et al. Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309, 948–951 (2005).
    1. Logothetis NK, Pauls J, Augath M, Trinath T. & Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).
    1. Goldman RI, Stern JM, Engel J Jr. & Cohen MS Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport 13, 2487–2492 (2002).
    1. Laufs H. et al. EEG-correlated fMRI of human alpha activity. Neuroimage 19, 1463–1476 (2003).
    1. Scheeringa R. et al. Trial-by-trial coupling between EEG and BOLD identifies networks related to alpha and theta EEG power increases during working memory maintenance. Neuroimage 44, 1224–1238 (2009).
    1. Ossandon T. et al. Transient suppression of broadband gamma power in the default-mode network is correlated with task complexity and subject performance. J. Neurosci 31, 14521–14530 (2011).

    1. Verret L. et al. Inhibitory interneuron deficit links altered network activity and cognitive dysfunction in Alzheimer model. Cell 149, 708–721 (2012).

      This study shows that impairments of inhibitory interneurons contribute to network hypersynchrony, altered oscillatory rhythms, and behavioural deficits in hAPP-J20 mice.

    1. Maheshwari A, Marks RL, Yu KM & Noebels JL Shift in interictal relative gamma power as a novel biomarker for drug response in two mouse models of absence epilepsy. Epilepsia 57, 79–88 (2016).
    1. Crooks VC, Lubben J, Petitti DB, Little D. & Chiu V. Social network, cognitive function, and dementia incidence among elderly women. Am. J. Public Health 98, 1221–1227 (2008).
    1. Buzsaki G. & Draguhn A. Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004).
    1. Hestrin S. & Galarreta M. Electrical synapses define networks of neocortical GABAergic neurons. Trends Neurosci. 28, 304–309 (2005).
    1. Lapray D. et al. Behavior-dependent specialization of identified hippocampal interneurons. Nat. Neurosci 15, 1265–1271 (2012).
    1. Fu Y. et al. A cortical circuit for gain control by behavioral state. Cell 156, 1139–1152 (2014).

    1. Sohal VS, Zhang F, Yizhar O. & Deisseroth K. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature 459, 698–702 (2009).

      This study shows that parvalbumin cell activity is mechanistically linked to gamma oscillations.

    1. Fazzari P. et al. Control of cortical GABA circuitry development by Nrg1 and ErbB4 signalling. Nature 464, 1376–1380 (2010).
    1. Kepecs A. & Fishell G. Interneuron cell types are fit to function. Nature 505, 318–326 (2014).
    1. Southwell DG et al. Interneurons from embryonic development to cell-based therapy. Science 344, 1240622 (2014).
    1. Han S. et al. Autistic-like behaviour in Scn1a+/− mice and rescue by enhanced GABA-mediated neurotransmission. Nature 489, 385–390 (2012).
    1. Chao HT et al. Dysfunction in GABA signalling mediates autism-like stereotypies and Rett syndrome phenotypes. Nature 468, 263–269 (2010).
    1. Hunt RF, Girskis KM, Rubenstein JL, Alvarez-Buylla A. & Baraban SC GABA progenitors grafted into the adult epileptic brain control seizures and abnormal behavior. Nat. Neurosci 16, 692–697 (2013).

    1. Tong LM et al. Inhibitory interneuron progenitor transplantation restores normal learning and memory in ApoE4 knock-in mice without or with Aβ accumulation. J. Neurosci 34, 9506–9515 (2014).

      This study shows that transplants of inhibitory interneurons restore learning and memory in APOE4-KI mice.

    1. Freund TF & Katona I. Perisomatic inhibition. Neuron 56, 33–42 (2007).
    1. Schmidt MJ et al. Modulation of behavioral networks by selective interneuronal inactivation. Mol. Psychiatry 19, 580–587 (2014).
    1. Kvitsiani D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).
    1. Cardin JA et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459, 663–667 (2009).
    1. Cramer PE et al. ApoE-directed therapeutics rapidly clear β-amyloid and reverse deficits in AD mouse models. Science 335, 1503–1506 (2012).
    1. Rubio SE et al. Accelerated aging of the GABAergic septohippocampal pathway and decreased hippocampal rhythms in a mouse model of Alzheimer’s disease. FASEB J. 26, 4458–4467 (2012).
    1. Gurevicius K, Lipponen A. & Tanila H. Increased cortical and thalamic excitability in freely moving APPswe/PS1dE9 mice modeling epileptic activity associated with Alzheimer’s disease. Cereb. Cortex 23, 1148–1158 (2013).
    1. Kemere C, Carr MF, Karlsson MP & Frank LM Rapid and continuous modulation of hippocampal network state during exploration of new places. PLoS ONE 8, e73114 (2013).
    1. Busche MA et al. Rescue of long-range circuit dysfunction in Alzheimer’s disease models. Nat. Neurosci 18, 1623–1630 (2015).
    1. Mander BA et al. β-Amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation. Nat. Neurosci 18, 1051–1057 (2015).
    1. van Deursen JA, Vuurman EF, van Kranen-Mastenbroek VH, Verhey FR & Riedel WJ 40-Hz steady state response in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 32, 24–30 (2011).
    1. van Deursen JA, Vuurman EF, Verhey FR, van Kranen-Mastenbroek VH & Riedel WJ Increased EEG gamma band activity in Alzheimer’s disease and mild cognitive impairment. J. Neural Transm. (Vienna) 115, 1301–1311 (2008).
    1. Nimmrich V, Draguhn A. & Axmacher N. Neuronal network oscillations in neurodegenerative diseases. Neuromolecular Med. 17, 270–284 (2015).

    1. Gillespie AK et al. Apolipoprotein E4 causes age-dependent disruption of slow gamma oscillations during hippocampal sharp-wave ripples. Neuron 90, 740–751 (2016).

      This study shows reduced gamma power during hippocampal sharp-wave ripples in APOE4-KI mice.

    1. Pena-Ortega F, Solis-Cisneros A, Ordaz B, Balleza-Tapia H. & Javier Lopez-Guerrero J. Amyloid beta 1–42 inhibits entorhinal cortex activity in the beta-gamma range: role of GSK-3. Curr. Alzheimer Res 9, 857–863 (2012).
    1. Driver JE et al. Impairment of hippocampal gamma-frequency oscillations in vitro in mice overexpressing human amyloid precursor protein (APP). Eur. J. Neurosci 26, 1280–1288 (2007).
    1. Villette V. et al. Decreased rhythmic GABAergic septal activity and memory–associated theta oscillations after hippocampal amyloid-β pathology in the rat. J. Neurosci 30, 10991–11003 (2010).
    1. Royer S. et al. Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition. Nat. Neurosci 15, 769–775 (2012).
    1. Tort AB, Komorowski RW, Manns JR, Kopell NJ & Eichenbaum H. Theta-gamma coupling increases during the learning of item-context associations. Proc. Natl Acad. Sci. USA 106, 20942–20947 (2009).
    1. Lakatos P, Karmos G, Mehta AD, Ulbert I. & Schroeder CE Entrainment of neuronal oscillations as a mechanism of attentional selection. Science 320, 110–113 (2008).
    1. Canolty RT et al. High gamma power is phase-locked to theta oscillations in human neocortex. Science 313, 1626–1628 (2006).
    1. Bragin A. et al. Gamma (40–100 Hz) oscillation in the hippocampus of the behaving rat. J. Neurosci 15, 47–60 (1995).
    1. Sirota A. et al. Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm. Neuron 60, 683–697 (2008).
    1. Freund TF Interneuron diversity series: Rhythm and mood in perisomatic inhibition. Trends Neurosci. 26, 489–495 (2003).
    1. Belluscio MA, Mizuseki K, Schmidt R, Kempter R. & Buzsaki G. Cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus. J. Neurosci 32, 423–435 (2012).
    1. Goutagny R. et al. Alterations in hippocampal network oscillations and theta-gamma coupling arise before Aβ overproduction in a mouse model of Alzheimer’s disease. Eur. J. Neurosci 37, 1896–1902 (2013).

    1. Klausberger T. et al. Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo. Nature 421, 844–848 (2003).

      This study shows that different types of interneurons contribute to specific oscillatory frequencies.

    1. Andrews-Zwilling Y. et al. Hilar GABAergic interneuron activity controls spatial learning and memory retrieval. PLoS ONE 7, e40555 (2012).
    1. Wang WZ et al. The developmental changes of Nav1.1 and Nav1.2 expression in the human hippocampus and temporal lobe. Brain Res. 1389, 61–70 (2011).
    1. Corbett BF et al. Sodium channel cleavage is associated with aberrant neuronal activity and cognitive deficits in a mouse model of Alzheimer’s disease. J. Neurosci 33, 7020–7026 (2013).
    1. Kim DY et al. BACE1 regulates voltage-gated sodium channels and neuronal activity. Nat. Cell Biol 9, 755–764 (2007).
    1. Yang LB et al. Elevated β-secretase expression and enzymatic activity detected in sporadic Alzheimer disease. Nat. Med 9, 3–4 (2003).
    1. Kim DY et al. The E280A presenilin mutation reduces voltage-gated sodium channel levels in neuronal cells. Neurodegener. Dis 13, 64–68 (2014).
    1. Ponomareva NV, Korovaitseva GI & Rogaev EI EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease. Neurobiol. Aging 29, 819–827 (2008).
    1. Andrews-Zwilling Y. et al. Apolipoprotein E4 causes age- and Tau-dependent impairment of GABAergic interneurons, leading to learning and memory deficits in mice. J. Neurosci 30, 13707–13717 (2010).
    1. Terry RD et al. Physical basis of cognitive alterations in Alzheimer’s disease: synapse loss is the major correlate of cognitive impairment. Ann. Neurol 30, 572–580 (1991).
    1. DeKosky ST & Scheff SW Synapse loss in frontal cortex biopsies in Alzheimer’s disease: correlation with cognitive severity. Ann. Neurol 27, 457–464 (1990).
    1. Walsh DM et al. Naturally secreted oligomers of amyloid β protein potently inhibit hippocampal long-term potentiation in vivo. Nature 416, 535–539 (2002).
    1. Hsia AY et al. Plaque-independent disruption of neural circuits in Alzheimer’s disease mouse models. Proc. Natl. Acad. Sci. USA 96, 3228–3233 (1999).
    1. Mucke L. et al. High-level neuronal expression of Aβ1–42 in wild-type human amyloid protein precursor transgenic mice: synaptotoxicity without plaque formation. J. Neurosci 20, 4050–4058 (2000).
    1. Kim JH, Anwyl R, Suh YH, Djamgoz MB & Rowan MJ Use-dependent effects of amyloidogenic fragments of β-amyloid precursor protein on synaptic plasticity in rat hippocampus in vivo. J. Neurosci 21, 1327–1333 (2001).
    1. Hsieh H. et al. AMPAR removal underlies Aβ-induced synaptic depression and dendritic spine loss. Neuron 52, 831–843 (2006).
    1. Palop JJ & Mucke L. Synaptic depression and aberrant excitatory network activity in Alzheimer’s disease: two faces of the same coin? Neuromolecular Med. 12, 48–55 (2010).
    1. Liu L. et al. Role of NMDA receptor subtypes in governing the direction of hippocampal synaptic plasticity. Science 304, 1021–1024 (2004).
    1. Campbell SL, Hablitz JJ & Olsen ML Functional changes in glutamate transporters and astrocyte biophysical properties in a rodent model of focal cortical dysplasia. Front. Cell. Neurosci 8, 425 (2014).
    1. Palop JJ et al. Neuronal depletion of calcium-dependent proteins in the dentate gyrus is tightly linked to Alzheimer’s disease-related cognitive deficits. Proc. Natl Acad. Sci. USA 100, 9572–9577 (2003).
    1. Cheng JS et al. Collagen VI protects neurons against Aβ toxicity. Nat. Neurosci 12, 119–121 (2009).
    1. Chin J. et al. Reelin depletion in the entorhinal cortex of human amyloid precursor protein transgenic mice and humans with Alzheimer’s disease. J. Neurosci 27, 2727–2733 (2007).
    1. Meilandt WJ et al. Enkephalin elevations contribute to neuronal and behavioral impairments in a transgenic mouse model of Alzheimer’s disease. J. Neurosci 28, 5007–5017 (2008).
    1. Orr AG et al. Astrocytic adenosine receptor A2A and Gs-coupled signaling regulate memory. Nat. Neurosci 18, 423–434 (2015).
    1. Albert MS et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 270–279 (2011).
    1. Gainotti G, Quaranta D, Vita MG & Marra C. Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease. J. Alzheimers Dis 38, 481–495 (2014).
    1. Budson AE & Solomon PR New criteria for Alzheimer disease and mild cognitive impairment: implications for the practicing clinician. Neurologist 18, 356–363 (2012).
    1. Jensen HS, Grunnet M. & Bastlund JF Therapeutic potential of Nav1.1 activators. Trends. Pharmacol. Sci 35, 113–118 (2014).
    1. Tuszynski MH et al. A phase 1 clinical trial of nerve growth factor gene therapy for Alzheimer disease. Nat. Med 11, 551–555 (2005).
    1. Knoferle J. et al. Apolipoprotein E4 produced in GABAergic interneurons causes learning and memory deficits in mice. J. Neurosci 34, 14069–14078 (2014).

    1. Buckner RL et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J. Neurosci 25, 7709–7717 (2005).

      This study shows that PiB+ amyloid preferentially deposits in brain regions of the DMN.

    1. Axelman K, Basun H, Winblad B. & Lannfelt L. A large Swedish family with Alzheimer’s disease with a codon 670/671 amyloid precursor protein mutation: a clinical and genealogical investigation. Arch. Neurol 51, 1193–1197 (1994).
    1. Godbolt AK et al. A second family with familial AD and the V717L APP mutation has a later age at onset. Neurology 66, 611–612 (2006).
    1. Abe M. et al. Phenotypical difference of amyloid precursor protein (APP) V717L mutation in Japanese family. BMC Neurol. 12, 38 (2012).
    1. Kennedy AM et al. Familial Alzheimer’s disease. A pedigree with a mis-sense mutation in the amyloid precursor protein gene (amyloid precursor protein 717 valine→glycine). Brain 116, 309–324 (1993).
    1. Mullan M. et al. Clinical comparison of Alzheimer’s disease in pedigrees with the codon 717 Val→Ile mutation in the amyloid precursor protein gene. Neurobiol. Aging 14, 407–419 (1993).
    1. Lindquist SG et al. Atypical early-onset Alzheimer’s disease caused by the Iranian APP mutation. J. Neurol. Sci 268, 124–130 (2008).
    1. Edwards-Lee T. et al. An African American family with early-onset Alzheimer disease and an APP (T714I) mutation. Neurology 64, 377–379 (2005).
    1. Sleegers K. et al. APP duplication is sufficient to cause early onset Alzheimer’s dementia with cerebral amyloid angiopathy. Brain 129, 2977–2983 (2006).
    1. Remes AM et al. Hereditary dementia with intracerebral hemorrhages and cerebral amyloid angiopathy. Neurology 63, 234–240 (2004).
    1. Rovelet-Lecrux A. et al. APP locus duplication in a Finnish family with dementia and intracerebral haemorrhage. J. Neurol. Neurosurg. Psychiatry 78, 1158–1159 (2007).
    1. Axelman K, Basun H. & Lannfelt L. Wide range of disease onset in a family with Alzheimer disease and a His163Tyr mutation in the presenilin-1 gene. Arch. Neurol 55, 698–702 (1998).
    1. Mangone CA et al. Early onset Alzheimer’s disease in a South American pedigree from Argentina. Acta Neurol. Scand 91, 6–13 (1995).
    1. Mann DMA, et al. Amyloid angiopathy and variability in amyloid β deposition is determined by mutation position in presenilin-1-linked Alzheimer’s disease. Am. J. Pathol 158, 2165–2175 (2001).
    1. Frommelt P, Schnabel R, Kuhne W, Nee LE & Polinsky RJ Familial Alzheimer disease: a large, multigeneration German kindred. Alzheimer Dis. Assoc. Disord 5, 36–43 (1991).
    1. Vossel KA et al. Incidence and impact of subclinical epileptiform activity in alzheimer’s disease. Ann. Neurol 10.1002/ana.24794 (2016).

Source: PubMed

Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

3
Abonner