Alterations of specific cortical GABAergic circuits underlie abnormal network activity in a mouse model of Down syndrome

Down syndrome is a genetic disorder caused by the presence of a third copy of chromosome 21. Affected individuals show delayed growth, characteristic facial features, altered brain development; with mild to severe intellectual disability. The exact mechanisms underlying the intellectual disability in Down syndrome are unclear, although studies in mice have provided clues. Drugs that reduce the inhibitory activity in the brain improve cognition in a mouse model of Down syndrome. This suggests that excessive inhibitory activity may contribute to the cognitive impairments.

Many different neural circuits generate inhibitory activity in the brain. These circuits contain cells called interneurons. Sub-types of interneurons act via different mechanisms to reduce the activity of neurons. Identifying the interneurons that are affected in Down syndrome would thus improve our understanding of the brain basis of the disorder.

Zorrilla de San Martin et al. compared mice with Down syndrome to unaffected control mice. The results revealed an increased activity in two types of inhibitory brain circuits in Down syndrome. The first contains interneurons called Martinotti cells. These help the brain to combine inputs from different sources. The second contains interneurons called parvalbumin-positive basket cells. These help different areas of the brain to synchronize their activity, which in turn makes it easier for those areas to exchange information.

By mapping the changes in inhibitory circuits in Down syndrome, Zorrilla de San Martin et al. have provided new insights into the biological basis of the disorder. Future studies should examine whether targeting specific circuits with pharmacological treatments could ultimately help reduce the associated impairments.

Down syndrome (DS) is a condition caused by full or partial trisomy of human chromosome 21, characterized by various physical and neurological features including mild to severe intellectual disability (Antonarakis et al., 2020). Individuals with DS present important deficits in cognitive tasks known to depend on the anatomical and functional integrity of the frontal lobe (Lee et al., 2015). Moreover, DS is also associated with other CNS-mediated phenotypes, including an ultra-high risk for developing Alzheimer’s disease and high rates of autism for which the mechanisms are unknown (DiGuiseppi et al., 2010; Wiseman et al., 2015). Interventions to ameliorate DS-mediated cognitive dysfunctions are limited. The development of interventions for this vulnerable group of individuals can be achieved through a better understanding of the mechanisms underlying a core feature of DS, such as intellectual disability.

Importantly, mouse models of DS recapitulate several cognitive deficits of this condition (Herault et al., 2017; Olmos-Serrano et al., 2016b). One of the best-characterized mouse models is the Ts65Dn mouse line (herein referred to as Ts), which carries a partial trisomy of a segment of the mouse chromosome 16 (Davisson et al., 1993). Ts mice recapitulate several dysfunctions present in DS individuals, such as reduced birthweight, male sterility, abnormal facial appearance and several cognitive impairments, including executive functions, such as working memory and cognitive flexibility (Olmos-Serrano et al., 2016b).

Recovery of behavioral and neurophysiological deficits underlying cognitive impairments using GABA_A receptor blockers led to the hypothesis that intellectual deficits in DS are produced by an excessive activity of inhibitory circuits (Fernandez et al., 2007; Zorrilla de San Martin et al., 2018). Nonetheless, direct evidence for over-inhibition in DS is lacking. Moreover, given the anatomical, molecular and functional diversity of cortical inhibitory neurons (Tremblay et al., 2016), the functional implications of this hypothesis at the network level, as well as the involvement of specific GABAergic circuits remain obscure.

Executive functions depend on the integrity of the prefrontal cortex (PFC), which plays an essential role in the synchronization of task-relevant, large-scale neuronal activity (Helfrich and Knight, 2016). An important network correlate of this synchronization is represented by neuronal oscillations: rhythmic fluctuations of the electrical activity of single neurons, local neuronal populations and multiple neuronal assemblies, distributed across different brain regions (Buzsáki and Wang, 2012). Oscillations are the result of a balanced and coordinated activity of excitatory pyramidal neurons (PNs) and a rich diversity of inhibitory neurons that use γ-aminobutiric acid (GABA) as neurotransmitter. In particular, parvalbumin (PV)-positive inhibitory interneurons form synapses onto the perisomatic region of PNs. PV cells thus tightly control PN spiking activity and drive network oscillations in the γ-frequency range (30–100 Hz) (Buzsáki and Wang, 2012). γ-Oscillations are necessary for several PFC cognitive functions, such as sustained attention (Kim et al., 2016b) and cognitive flexibility (Cho et al., 2015). Conversely, Martinotti cells (MCs) are somatostatin (SST)-positive interneurons that inhibit distal dendrites of PNs, thereby controlling the integration of distal dendritic glutamatergic synaptic inputs originating from different regions of the brain (Tremblay et al., 2016). Dendritic integration of multi-pathway inputs is necessary for working memory (Abbas et al., 2018; Kim et al., 2016a). Therefore, PV interneurons and MCs represent two major cortical inhibitory circuits, characterized by a precise division of labor during cortical activity. Both forms of inhibition were shown to be involved in the entrainment of network oscillations (Cardin et al., 2009; Chen et al., 2017; Sohal et al., 2009; Veit et al., 2017) and in the cognitive performance during medial (m)PFC-dependent tasks (Abbas et al., 2018; Cho et al., 2015; Cummings and Clem, 2020). In particular, inhibition from SST interneurons plays a crucial role in mPFC-dependent memory (Abbas et al., 2018; Cummings and Clem, 2020).

Broad-spectrum GABA_AR antagonists are not clinically viable, as they can yield undesired seizure-like activity and/or anxiety. Interestingly, however, treatment of Ts mice with selective and partial negative allosteric modulators of α5-containing GABA_ARs (α5 inverse agonist or α5IA) reverse cognitive behavioral and long-term synaptic plasticity deficits in DS mice (Braudeau et al., 2011; Duchon et al., 2020; Martínez-Cué et al., 2013; Schulz et al., 2019). Importantly, neocortical dendritic synaptic inhibition of PNs from MCs relies on α5-containing GABA_ARs (Ali and Thomson, 2008). The preference for this specific GABA_AR subunit was also recently demonstrated at the equivalent hippocampal dendritic inhibitory circuit (Schulz et al., 2019; Schulz et al., 2018), raising the question of whether dendritic inhibition is specifically altered in DS.

Here we found that the dendritic synaptic inhibitory loop formed by MCs and PNs was strongly potentiated in Ts mice, with no alteration of either cell-type excitability. Conversely, the perisomatic synaptic inhibitory loop from PV cells onto PN cell bodies was unaffected in Ts mice. Strikingly, however, PV-cell excitability was strongly altered: these interneurons did not display their typical fast-spiking behavior and exhibited enhanced excitability. At the network level in vivo, these inhibitory microcircuit-specific alterations resulted in significant reduction of putative PN firing, which in turn was more tuned to β- and low γ-oscillations (10–60 Hz). These results confirm over-inhibition in DS, and reveal unexpected functional alterations of specific GABAergic circuits in this condition.

In this study, we analyzed synaptic and intrinsic properties of two major inhibitory circuits of the prefrontal cortex in a relevant mouse model of DS. We found specific alterations of distinct GABAergic circuits. In particular, we demonstrate that the dendritic inhibitory synaptic loop involving MCs and PNs was strongly potentiated in Ts, as compared to euploid control mice. In contrast, the perisomatic synaptic inhibitory control of PNs by PV cells was not affected in Ts mice. Strikingly, however, the excitability of PV cells was profoundly altered. These GABAergic circuit-specific alterations correlate with reduced PN spiking, and enhanced coupling with network β-γ-activity in Ts mice in vivo.

We analyzed MCs, which are a subset of SST interneurons, specialized in inhibiting the distal portion of apical dendrites of PNs, by ascending their axons in L1 (Kawaguchi and Kubota, 1998), where PN dendrites integrate top-down input originating from distal brain areas. In the hippocampus, dendritic inhibition strongly regulates PN dendrite electrogenesis and supra-linearity (Lovett-Barron et al., 2012), likely modulating the emergence of burst firing (Royer et al., 2012). Interestingly, α5–mediated dendritic inhibition in the hippocampus, known to strongly control dendritic integration and action potential firing (Schulz et al., 2018), is also enhanced in Ts65Dn mice and strongly control NMDAR activation, nonlinear dendritic integration, and AP firing (Schulz et al., 2019). In the neocortex, integration of top-down information in L1 and consequent dendrite-dependent generation of burst activity was hypothesized to underlie the encoding of context-rich, salient information (Larkum, 2013). Our results indicate a specific synaptic strengthening of the dendritic inhibitory loop involving MCs in Ts mice, suggesting a major impact on PN dendritic integration and electrogenesis.

Enhanced dendritic inhibition by MCs in DS could underlie the deficits of long-term plasticity of glutamatergic synapses similar to those observed in the hippocampus of Ts mice. Indeed, these LTP deficits can be recovered by treatment of allosteric modulators of α5-GABA_ARs (Duchon et al., 2020; Martínez-Cué et al., 2013; Schulz et al., 2019). Likewise, the enhanced MC-PN inhibitory loop in Ts mice shown here can provide a mechanism for the rescue of cognitive deficits in Ts mice, operated by selective pharmacology of α5-GABA_ARs (Braudeau et al., 2011; Duchon et al., 2020; Martínez-Cué et al., 2013). This subunit of GABA_ARs, whose synaptic vs. extrasynaptic expression is still debated (Ali and Thomson, 2008; Botta et al., 2015; Glykys and Mody, 2010; Hannan et al., 2020; Hausrat et al., 2015; Schulz et al., 2018; Serwanski et al., 2006), mediate dendritic synaptic inhibition from neocortical MCs (Ali and Thomson, 2008) and their hippocampal counterparts (the oriens-lacunosum moleculare or O-LM interneurons, Schulz et al., 2018). Here, we confirmed that α5-containing GABA_ARs are majorly responsible for MC-PN dendritic synaptic inhibition, due to the overall block of α5IA, which was close to the max potency of this drug (Dawson et al., 2006).

The enhancement of the dendritic inhibitory loop involving MCs and PNs in Ts mice could not be attributed to alterations of axonal and/or dendritic arborizations in the mutant mice. On the other hand, it could be due to a combination of pre- and postsynaptic mechanisms, including alterations of release probability, number of release sites or quantal size at either GABAergic or glutamatergic synapses involved in this circuit. The strong increase of synaptic charge at MC-PN GABAergic synapses could be attributed to alterations of uIPSC waveform in Ts mice. Although the small change of uIPSC rise time can in principle account for, at least in part, an increase in synaptic charge, it is unlikely to account for the 5-fold increase of synaptic charge, with no changes in uIPSC decay.

The robust increase of synaptic charge in Ts mice is likely due to one or a combination of different non-linearities that dynamically emerge during a spike train. These include the recruitment of peri- or extrasynaptic GABA_ARs, possible alterations of release probability or quantal size during trains and differences in postsynaptic dendritic filtering in the two genotypes. These complex interplays between these factors occur at distal dendrites; therefore, they are difficult to tease out from somatic recordings as they are characterized by poor dendritic voltage control due to space-clamping constraints. The enhanced short-term depression of MC-PN uIPSCs that we report here suggests that release probability at dendritic GABAergic synapses from MCs is increased in Ts mice. This is consistent with increased short-term depression of inhibition reported in the dentate gyrus (Kleschevnikov et al., 2012), but not with unaltered short-term plasticity shown in CA1 (Mitra et al., 2012). These discrepancies could be ascribed to differences between brain regions. Moreover, they could also be a consequence of non-specific recruitment of presynaptic axons by global extracellular stimulation, in contrast to the isolation of unitary synaptic responses by dual intracellular recordings as reported here. The lack of short-term plasticity alterations at PN-MC glutamatergic synapses suggests that increased excitatory recruitment of MCs is due to alterations at postsynaptic sites. Future studies will be necessary to pinpoint the exact biophysical and anatomical alterations underlying the prominent increase of dendritic inhibition operated by MCs in DS.

Potentiation of glutamatergic synapses in Ts mice seems to be specific for PN-MC connections, as PN-PV synapses were similar in both genotypes, suggesting that presynaptic terminals of local glutamatergic synapses can undergo target-specific modulation of their strength. Intriguingly, it has been recently shown that an increase of the excitatory drive of hippocampal interneurons (due to triplication of GluR5 kainate receptor expression) could explain excess of inhibition received by pyramidal neurons in the Ts2Cje Down syndrome mouse model (Valbuena et al., 2019). Therefore, a similar gene overdose of kainate receptors can boost the recruitment of specific interneurons in the PFC of Ts mice.

The strong increase of membrane resistance of PV cells in Ts mice underlies the augmented intrinsic excitability and can produce early ectopic bouts of activity, thus contributing to network over-inhibition. Enhanced membrane resistance could result from alterations in the expression of TWIK1 and TASK1 leak channels. Indeed, these channels underlie the developmental decrease of membrane resistance in PV cells (Okaty et al., 2009). In contrast, the inability of PV cells of Ts mice to sustain high frequency firing could prevent these interneurons from generating high-frequency bursts of action potentials and therefore have a detrimental effect on the temporal coding of these interneurons.

Interestingly, however, despite the dramatic alterations of intrinsic excitability in Ts PV cells, their output synaptic perisomatic inhibition was similar in both genotypes. This, despite the widening of single PV-cell action potentials, which could, in principle change the presynaptic Ca²⁺ dynamics and thus alter release probability. Since action potentials are recorded in the soma, the lack of effect at PV-cell synapses could be due to soma-specific alterations. Alternatively, since neocortical PV-PN GABAergic synapses exhibit high release probability (Deleuze et al., 2019; Kawaguchi and Kubota, 1998), alterations of spike width might not be enough to produce additional increases. Increase of action potential width in Ts mice could be due to changes in the expression Kv3.1b potassium channels, known to underlie the fast repolarization of action potentials and fast-spiking behavior of PV cells (Erisir et al., 1999). Future experiments will be necessary to reveal the exact molecular mechanism underlying the altered firing properties of PV cells, which, in Ts mice, have lost their characteristic fast-spiking signature.

The aberrant active and passive properties of PV cells in Ts mice could be due to a delayed development of these interneurons. Indeed, during development, neocortical PV cells display marked accelerations of single action potentials and increased firing frequency, accompanied by decreased input resistance (Okaty et al., 2009). Although we cannot rule out the possibility of a delayed development of PV cells in Ts mice, these interneurons showed abnormal passive and active properties within the entire age range studied here. It will be interesting to determine whether these profound alterations of PV-cell firing are present with the same incidence and magnitude along the entire life span of Ts mice. Although previous reports have shown higher number of inhibitory interneurons in the hippocampus (Hernández-González et al., 2015) and somatosensory cortex (Aziz et al., 2018; Chakrabarti et al., 2010) we failed to detect significant differences in the density of both SST- and PV-positive interneurons in the mPFC. This could be due to differences between brain regions. A systematic comparative analysis will be required to better understand the consequences of DS neurodevelopmental alterations of the cellular composition in different brain regions.

Both potentiation of the dendritic inhibitory loop and increased PV-cell excitability are consistent with the alterations of PN spiking activity that we recorded in vivo. Indeed, reduced spike rates and increased tuning in the β-γ-frequency range are both consistent with increased activity of inhibitory neurons (Atallah et al., 2012; Cardin et al., 2009; Chen et al., 2017; Sohal et al., 2009; Veit et al., 2017). The overall LFP power spectra observed in mPFC is similar in both Ts and Eu mice, consistently with a recent report (Chang et al., 2020). However, close examination of stLFP revealed a strong increase in the power of β- and low γ-frequency bands during periods of spiking activity. This likely reflects the consequences of alterations at the level of local microcircuits.

We cannot directly link the inhibitory circuit-specific alterations that we detected in slices with the increased synchronization of β-γ-activity that we measured in vivo. However, a large body of literature indicates that oscillations in this frequency range strongly depends on the activity of PV cells (Buzsáki and Wang, 2012; Sohal et al., 2009; Cardin et al., 2009). More recently, also SST interneurons were shown to control PN phase coupling with low frequency (30 Hz) neocortical γ-oscillations (Chen et al., 2017; Veit et al., 2017). It is therefore tempting to speculate that increased dendritic inhibition from SST-expressing MCs modulates phase coupling of PNs with β- and low γ-oscillations. On the other hand, the increased phase coupling of PN spikes with high-frequency γ-activity could result from the augmented excitability of PV cells. The differential phase coupling of these two interneuron types at distinct frequencies is consistent with the peculiar fast and slower recruitment and biophysical properties of PV interneurons and MCs, respectively. Future experiments involving chemogenetic alterations of PV-interneuron excitability and/or pharmacological manipulations of MC-PN synapses in Ts mice will help decipher the role played by each inhibitory cell subtype in controlling the temporal dynamics of PN firing during different rhythmic cortical network activities. Alternatively, our results could be interpreted as differences in long-range functional connectivity in the two genotypes, possibly due to alterations in myelination and conduction velocity in Ts vs. Eu mice (Olmos-Serrano et al., 2016a). Nevertheless, a reduction of axonal conduction velocity would produce a temporal shift in spike-to-phase association, rather than increased phase locking.

The increase in β-γ-band power and enhanced neural synchronization in these frequency ranges in Ts mice is consistent with recent evidence indicating augmented hippocampal-PFC synchronization and LFP γ-band power during natural non-REM sleep in Ts65Dn mice (Alemany-González et al., 2020). This suggests that the alterations of γ-oscillations observed here could play a role in the pathophysiology of sleep disruptions reported in DS children (Fernandez et al., 2017) and adults (Giménez et al., 2018).

In sum, here we report direct evidence for over-inhibition of mPFC circuits in a mouse model of DS. However, over-inhibition was not due to a generic increase of GABAergic signaling, but emerged from highly specific synaptic and intrinsic alterations of dendritic and somatic inhibitory circuits, respectively. Future experiments are necessary to reveal whether other inhibitory neuron types are also affected in DS. Likewise, it will be fundamental to assess whether specific dysfunctions of individual GABAergic circuits underlie different aspects of cognitive deficits (e.g. impaired memory and flexibility, autistic traits), which affect individuals with DS.

Experimental procedures followed national and European (2010/63/EU) guidelines, and have been approved by the authors' institutional review boards and national authorities. All efforts were made to minimize suffering and reduce the number of animals. Experiments were performed on >4 week old and 18- to 25-day-old mice for in vivo and ex vivo recordings, respectively. We used B6EiC3Sn a/A-Ts(17 ¹⁶)65Dn/J (also known as Ts65Dn) mice (The Jackson Laboratory, Bar Harbor, Maine; stock #: 001924). For ex vivo slice experiments to label SST-positive MCs, Ts65Dn mice were crossed with Tg(Gad1-EGFP)98Agmo/J, also known as GAD67-GFP X98 or GFP-X98 (Jackson Laboratory). To label PV-INs, Ts65Dn mice were crossed with C57BL/6-Tg(Pvalb-tdTomato)15Gfng/J (Pvalb-tdTomato, Jackson Laboratory). Mice used in this study were of both sexes.

Source data files have been provided for: Figure 1, Figure 1–figure supplement 2, Figure 1–figure supplement 3, Figure 2, Figure 2–figure supplement 1, Figure 2–figure supplement 2, Figure 3, Figure 3–figure supplement 1, Figure 4, Figure 4–figure supplement 1 and Figure 5.

1. 1. Abbas AI
   2. Sundiang MJM
   3. Henoch B
   4. Morton MP
   5. Bolkan SS
   6. Park AJ
   7. Harris AZ
   8. Kellendonk C
   9. Gordon JA

   (2018) Somatostatin interneurons facilitate Hippocampal-Prefrontal synchrony and prefrontal spatial encoding

   Neuron 100:926–939.

   https://doi.org/10.1016/j.neuron.2018.09.029

   • Google Scholar
2. 1. Alemany-González M
   2. Gener T
   3. Nebot P
   4. Vilademunt M
   5. Dierssen M
   6. Puig MV

   (2020) Prefrontal-hippocampal functional connectivity encodes recognition memory and is impaired in intellectual disability

   PNAS 117:11788–11798.

   https://doi.org/10.1073/pnas.1921314117

   • PubMed
   • Google Scholar
3. 1. Ali AB
   2. Thomson AM

   (2008) Synaptic alpha 5 subunit-containing GABAA receptors mediate IPSPs elicited by dendrite-preferring cells in rat neocortex

   Cerebral Cortex 18:1260–1271.

   https://doi.org/10.1093/cercor/bhm160

   • PubMed
   • Google Scholar
4. 1. Antonarakis SE
   2. Skotko BG
   3. Rafii MS
   4. Strydom A
   5. Pape SE
   6. Bianchi DW
   7. Sherman SL
   8. Reeves RH

   (2020) Down syndrome

   Nature Reviews Disease Primers 6:7.

   https://doi.org/10.1038/s41572-019-0143-7

   • PubMed
   • Google Scholar
5. 1. Atallah BV
   2. Bruns W
   3. Carandini M
   4. Scanziani M

   (2012) Parvalbumin-expressing interneurons linearly transform cortical responses to visual stimuli

   Neuron 73:159–170.

   https://doi.org/10.1016/j.neuron.2011.12.013

   • PubMed
   • Google Scholar
6. 1. Aziz NM
   2. Guedj F
   3. Pennings JLA
   4. Olmos-Serrano JL
   5. Siegel A
   6. Haydar TF
   7. Bianchi DW

   (2018) Lifespan analysis of brain development, gene expression and behavioral phenotypes in the Ts1Cje, Ts65Dn and dp(16)1/Yey mouse models of down syndrome

   Disease Models & Mechanisms 11:dmm031013.

   https://doi.org/10.1242/dmm.031013

   • Google Scholar
7. 1. Botta P
   2. Demmou L
   3. Kasugai Y
   4. Markovic M
   5. Xu C
   6. Fadok JP
   7. Lu T
   8. Poe MM
   9. Xu L
   10. Cook JM
   11. Rudolph U
   12. Sah P
   13. Ferraguti F
   14. Lüthi A

   (2015) Regulating anxiety with extrasynaptic inhibition

   Nature Neuroscience 18:1493–1500.

   https://doi.org/10.1038/nn.4102

   • PubMed
   • Google Scholar
8. 1. Braudeau J
   2. Delatour B
   3. Duchon A
   4. Pereira PL
   5. Dauphinot L
   6. de Chaumont F
   7. Olivo-Marin JC
   8. Dodd RH
   9. Hérault Y
   10. Potier MC

   (2011) Specific targeting of the GABA-A receptor α5 subtype by a selective inverse agonist restores cognitive deficits in down syndrome mice

   Journal of Psychopharmacology 25:1030–1042.

   https://doi.org/10.1177/0269881111405366

   • PubMed
   • Google Scholar
9. 1. Buzsáki G
   2. Wang XJ

   (2012) Mechanisms of gamma oscillations

   Annual Review of Neuroscience 35:203–225.

   https://doi.org/10.1146/annurev-neuro-062111-150444

   • PubMed
   • Google Scholar
10. 1. Cardin JA
    2. Carlén M
    3. Meletis K
    4. Knoblich U
    5. Zhang F
    6. Deisseroth K
    7. Tsai LH
    8. Moore CI

    (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses

    Nature 459:663–667.

    https://doi.org/10.1038/nature08002

    • PubMed
    • Google Scholar
11. 1. Chakrabarti L
    2. Best TK
    3. Cramer NP
    4. Carney RS
    5. Isaac JT
    6. Galdzicki Z
    7. Haydar TF

    (2010) Olig1 and Olig2 triplication causes developmental brain defects in down syndrome

    Nature Neuroscience 13:927–934.

    https://doi.org/10.1038/nn.2600

    • PubMed
    • Google Scholar
12. 1. Chang P
    2. Bush D
    3. Schorge S
    4. Good M
    5. Canonica T
    6. Shing N
    7. Noy S
    8. Wiseman FK
    9. Burgess N
    10. Tybulewicz VLJ
    11. Walker MC
    12. Fisher EMC

    (2020) Altered Hippocampal-Prefrontal neural dynamics in mouse models of down syndrome

    Cell Reports 30:1152–1163.

    https://doi.org/10.1016/j.celrep.2019.12.065

    • PubMed
    • Google Scholar
13. 1. Chen G
    2. Zhang Y
    3. Li X
    4. Zhao X
    5. Ye Q
    6. Lin Y
    7. Tao HW
    8. Rasch MJ
    9. Zhang X

    (2017) Distinct inhibitory circuits orchestrate cortical beta and gamma band oscillations

    Neuron 96:1403–1418.

    https://doi.org/10.1016/j.neuron.2017.11.033

    • PubMed
    • Google Scholar
14. 1. Cho KK
    2. Hoch R
    3. Lee AT
    4. Patel T
    5. Rubenstein JL
    6. Sohal VS

    (2015) Gamma rhythms link prefrontal interneuron dysfunction with cognitive inflexibility in dlx5/6+/-mice

    Neuron 85:1332–1343.

    https://doi.org/10.1016/j.neuron.2015.02.019

    • PubMed
    • Google Scholar
15. 1. Cummings KA
    2. Clem RL

    (2020) Prefrontal somatostatin interneurons encode fear memory

    Nature Neuroscience 23:61.

    https://doi.org/10.1038/s41593-019-0552-7

    • PubMed
    • Google Scholar
16. 1. Davisson MT
    2. Schmidt C
    3. Reeves RH
    4. Irving NG
    5. Akeson EC
    6. Harris BS
    7. Bronson RT

    (1993)

    Segmental trisomy as a mouse model for down syndrome

    Progress in Clinical and Biological Research 384:117–133.

    • PubMed
    • Google Scholar
17. 1. Dawson GR
    2. Maubach KA
    3. Collinson N
    4. Cobain M
    5. Everitt BJ
    6. MacLeod AM
    7. Choudhury HI
    8. McDonald LM
    9. Pillai G
    10. Rycroft W
    11. Smith AJ
    12. Sternfeld F
    13. Tattersall FD
    14. Wafford KA
    15. Reynolds DS
    16. Seabrook GR
    17. Atack JR

    (2006) An inverse agonist selective for alpha5 subunit-containing GABAA receptors enhances cognition

    The Journal of Pharmacology and Experimental Therapeutics 316:1335–1345.

    https://doi.org/10.1124/jpet.105.092320

    • PubMed
    • Google Scholar
18. 1. Deleuze C
    2. Bhumbra GS
    3. Pazienti A
    4. Lourenço J
    5. Mailhes C
    6. Aguirre A
    7. Beato M
    8. Bacci A

    (2019) Strong preference for autaptic self-connectivity of neocortical PV interneurons facilitates their tuning to γ-oscillations

    PLOS Biology 17:e3000419.

    https://doi.org/10.1371/journal.pbio.3000419

    • PubMed
    • Google Scholar
19. 1. DiGuiseppi C
    2. Hepburn S
    3. Davis JM
    4. Fidler DJ
    5. Hartway S
    6. Lee NR
    7. Miller L
    8. Ruttenber M
    9. Robinson C

    (2010) Screening for autism spectrum disorders in children with down syndrome: population prevalence and screening test characteristics

    Journal of Developmental and Behavioral Pediatrics : JDBP 31:181–191.

    https://doi.org/10.1097/DBP.0b013e3181d5aa6d

    • PubMed
    • Google Scholar
20. 1. Duchon A
    2. Gruart A
    3. Albac C
    4. Delatour B
    5. Zorrilla de San Martin J
    6. Delgado-García JM
    7. Hérault Y
    8. Potier MC

    (2020) Long-lasting correction of in vivo LTP and cognitive deficits of mice modelling down syndrome with an α5-selective GABAA inverse agonist

    British Journal of Pharmacology 177:1106–1118.

    https://doi.org/10.1111/bph.14903

    • PubMed
    • Google Scholar
21. 1. Erisir A
    2. Lau D
    3. Rudy B
    4. Leonard CS

    (1999) Function of specific K(+) channels in sustained high-frequency firing of fast-spiking neocortical interneurons

    Journal of Neurophysiology 82:2476–2489.

    https://doi.org/10.1152/jn.1999.82.5.2476

    • PubMed
    • Google Scholar
22. 1. Fernandez F
    2. Morishita W
    3. Zuniga E
    4. Nguyen J
    5. Blank M
    6. Malenka RC
    7. Garner CC

    (2007) Pharmacotherapy for cognitive impairment in a mouse model of down syndrome

    Nature Neuroscience 10:411–413.

    https://doi.org/10.1038/nn1860

    • PubMed
    • Google Scholar
23. 1. Fernandez F
    2. Nyhuis CC
    3. Anand P
    4. Demara BI
    5. Ruby NF
    6. Spanò G
    7. Clark C
    8. Edgin JO

    (2017) Young children with down syndrome show normal development of circadian rhythms, but poor sleep efficiency: a cross-sectional study across the first 60 months of life

    Sleep Medicine 33:134–144.

    https://doi.org/10.1016/j.sleep.2016.12.026

    • PubMed
    • Google Scholar
24. 1. Giménez S
    2. Videla L
    3. Romero S
    4. Benejam B
    5. Clos S
    6. Fernández S
    7. Martínez M
    8. Carmona-Iragui M
    9. Antonijoan RM
    10. Mayos M
    11. Fortuna A
    12. Peñacoba P
    13. Plaza V
    14. Osorio RS
    15. Sharma RA
    16. Bardés I
    17. Rebillat AS
    18. Lleó A
    19. Blesa R
    20. Videla S
    21. Fortea J

    (2018) Prevalence of sleep disorders in adults with down syndrome: a comparative study of Self-Reported, Actigraphic, and polysomnographic findings

    Journal of Clinical Sleep Medicine 14:1725–1733.

    https://doi.org/10.5664/jcsm.7382

    • PubMed
    • Google Scholar
25. 1. Glykys J
    2. Mody I

    (2010) Hippocampal network hyperactivity after selective reduction of tonic inhibition in GABA A receptor ␣ 5 subunit – Deficient Mice

    Journal of Neurophysiology 2005:2796–2807.

    https://doi.org/10.1152/jn.01122.2005

    • Google Scholar
26. 1. Hannan S
    2. Minere M
    3. Harris J
    4. Izquierdo P
    5. Thomas P
    6. Tench B
    7. Smart TG

    (2020) GABAAR isoform and subunit structural motifs determine synaptic and extrasynaptic receptor localisation

    Neuropharmacology 169:107540.

    https://doi.org/10.1016/j.neuropharm.2019.02.022

    • PubMed
    • Google Scholar
27. 1. Hausrat TJ
    2. Muhia M
    3. Gerrow K
    4. Thomas P
    5. Hirdes W
    6. Tsukita S
    7. Heisler FF
    8. Herich L
    9. Dubroqua S
    10. Breiden P
    11. Feldon J
    12. Schwarz JR
    13. Yee BK
    14. Smart TG
    15. Triller A
    16. Kneussel M

    (2015) Radixin regulates synaptic GABAA receptor density and is essential for reversal learning and short-term memory

    Nature Communications 6:6872.

    https://doi.org/10.1038/ncomms7872

    • PubMed
    • Google Scholar
28. 1. Helfrich RF
    2. Knight RT

    (2016) Oscillatory dynamics of prefrontal cognitive control

    Trends in Cognitive Sciences 20:916–930.

    https://doi.org/10.1016/j.tics.2016.09.007

    • PubMed
    • Google Scholar
29. 1. Herault Y
    2. Delabar JM
    3. Fisher EMC
    4. Tybulewicz VLJ
    5. Yu E
    6. Brault V

    (2017) Rodent models in down syndrome research: impact and future opportunities

    Disease Models & Mechanisms 10:1165–1186.

    https://doi.org/10.1242/dmm.029728

    • PubMed
    • Google Scholar
30. 1. Hernández-González S
    2. Ballestín R
    3. López-Hidalgo R
    4. Gilabert-Juan J
    5. Blasco-Ibáñez JM
    6. Crespo C
    7. Nácher J
    8. Varea E

    (2015) Altered distribution of hippocampal interneurons in the murine down syndrome model Ts65Dn

    Neurochemical Research 40:151–164.

    https://doi.org/10.1007/s11064-014-1479-8

    • PubMed
    • Google Scholar
31. 1. Kaiser T
    2. Ting JT
    3. Monteiro P
    4. Feng G

    (2016) Transgenic labeling of parvalbumin-expressing neurons with tdTomato

    Neuroscience 321:236–245.

    https://doi.org/10.1016/j.neuroscience.2015.08.036

    • PubMed
    • Google Scholar
32. 1. Kawaguchi Y
    2. Kubota Y

    (1998) Neurochemical features and synaptic connections of large physiologically-identified GABAergic cells in the rat frontal cortex

    Neuroscience 85:677–701.

    https://doi.org/10.1016/S0306-4522(97)00685-4

    • PubMed
    • Google Scholar
33. 1. Kim D
    2. Jeong H
    3. Lee J
    4. Ghim JW
    5. Her ES
    6. Lee SH
    7. Jung MW

    (2016a) Distinct roles of parvalbumin- and Somatostatin-Expressing interneurons in working memory

    Neuron 92:902–915.

    https://doi.org/10.1016/j.neuron.2016.09.023

    • PubMed
    • Google Scholar
34. 1. Kim H
    2. Ährlund-Richter S
    3. Wang X
    4. Deisseroth K
    5. Carlén M

    (2016b) Prefrontal parvalbumin neurons in control of attention

    Cell 164:208–218.

    https://doi.org/10.1016/j.cell.2015.11.038

    • PubMed
    • Google Scholar
35. 1. Kleschevnikov AM
    2. Belichenko PV
    3. Gall J
    4. George L
    5. Nosheny R
    6. Maloney MT
    7. Salehi A
    8. Mobley WC

    (2012) Increased efficiency of the GABAA and GABAB receptor-mediated neurotransmission in the Ts65Dn mouse model of down syndrome

    Neurobiology of Disease 45:683–691.

    https://doi.org/10.1016/j.nbd.2011.10.009

    • PubMed
    • Google Scholar
36. 1. Larkum M

    (2013) A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex

    Trends in Neurosciences 36:141–151.

    https://doi.org/10.1016/j.tins.2012.11.006

    • PubMed
    • Google Scholar
37. 1. Lee NR
    2. Anand P
    3. Will E
    4. Adeyemi EI
    5. Clasen LS
    6. Blumenthal JD
    7. Giedd JN
    8. Daunhauer LA
    9. Fidler DJ
    10. Edgin JO

    (2015) Everyday executive functions in down syndrome from early childhood to young adulthood: evidence for both unique and shared characteristics compared to youth with sex chromosome trisomy (XXX and XXY)

    Frontiers in Behavioral Neuroscience 9:264.

    https://doi.org/10.3389/fnbeh.2015.00264

    • PubMed
    • Google Scholar
38. 1. Lovett-Barron M
    2. Turi GF
    3. Kaifosh P
    4. Lee PH
    5. Bolze F
    6. Sun XH
    7. Nicoud JF
    8. Zemelman BV
    9. Sternson SM
    10. Losonczy A

    (2012) Regulation of neuronal input transformations by tunable dendritic inhibition

    Nature Neuroscience 15:423–430.

    https://doi.org/10.1038/nn.3024

    • PubMed
    • Google Scholar
39. 1. Ma Y
    2. Hu H
    3. Berrebi AS
    4. Mathers PH
    5. Agmon A

    (2006) Distinct subtypes of somatostatin-containing neocortical interneurons revealed in transgenic mice

    Journal of Neuroscience 26:5069–5082.

    https://doi.org/10.1523/JNEUROSCI.0661-06.2006

    • PubMed
    • Google Scholar
40. 1. Martínez-Cué C
    2. Martínez P
    3. Rueda N
    4. Vidal R
    5. García S
    6. Vidal V
    7. Corrales A
    8. Montero JA
    9. Pazos Á
    10. Flórez J
    11. Gasser R
    12. Thomas AW
    13. Honer M
    14. Knoflach F
    15. Trejo JL
    16. Wettstein JG
    17. Hernández MC

    (2013) Reducing GABAA α5 receptor-mediated inhibition rescues functional and neuromorphological deficits in a mouse model of down syndrome

    Journal of Neuroscience 33:3953–3966.

    https://doi.org/10.1523/JNEUROSCI.1203-12.2013

    • PubMed
    • Google Scholar
41. 1. Mitra A
    2. Blank M
    3. Madison DV

    (2012) Developmentally altered inhibition in Ts65Dn, a mouse model of down syndrome

    Brain Research 1440:1–8.

    https://doi.org/10.1016/j.brainres.2011.12.034

    • PubMed
    • Google Scholar
42. 1. Okaty BW
    2. Miller MN
    3. Sugino K
    4. Hempel CM
    5. Nelson SB

    (2009) Transcriptional and electrophysiological maturation of neocortical fast-spiking GABAergic interneurons

    Journal of Neuroscience 29:7040–7052.

    https://doi.org/10.1523/JNEUROSCI.0105-09.2009

    • PubMed
    • Google Scholar
43. 1. Olmos-Serrano JL
    2. Kang HJ
    3. Tyler WA
    4. Silbereis JC
    5. Cheng F
    6. Zhu Y
    7. Pletikos M
    8. Jankovic-Rapan L
    9. Cramer NP
    10. Galdzicki Z
    11. Goodliffe J
    12. Peters A
    13. Sethares C
    14. Delalle I
    15. Golden JA
    16. Haydar TF
    17. Sestan N

    (2016a) Down syndrome developmental brain transcriptome reveals defective oligodendrocyte differentiation and myelination

    Neuron 89:1208–1222.

    https://doi.org/10.1016/j.neuron.2016.01.042

    • PubMed
    • Google Scholar
44. 1. Olmos-Serrano JL
    2. Tyler WA
    3. Cabral HJ
    4. Haydar TF

    (2016b) Longitudinal measures of cognition in the Ts65Dn mouse: refining windows and defining modalities for therapeutic intervention in down syndrome

    Experimental Neurology 279:40–56.

    https://doi.org/10.1016/j.expneurol.2016.02.005

    • PubMed
    • Google Scholar
45. 1. Perrenoud Q
    2. Pennartz CM
    3. Gentet LJ

    (2016) Membrane potential dynamics of spontaneous and visually evoked gamma activity in V1 of awake mice

    PLOS Biology 14:e1002383.

    https://doi.org/10.1371/journal.pbio.1002383

    • PubMed
    • Google Scholar
46. 1. Royer S
    2. Zemelman BV
    3. Losonczy A
    4. Kim J
    5. Chance F
    6. Magee JC
    7. Buzsáki G

    (2012) Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition

    Nature Neuroscience 15:769–775.

    https://doi.org/10.1038/nn.3077

    • PubMed
    • Google Scholar
47. 1. Ruiz-Mejias M
    2. Ciria-Suarez L
    3. Mattia M
    4. Sanchez-Vives MV

    (2011) Slow and fast rhythms generated in the cerebral cortex of the anesthetized mouse

    Journal of Neurophysiology 106:2910–2921.

    https://doi.org/10.1152/jn.00440.2011

    • PubMed
    • Google Scholar
48. 1. Schulz JM
    2. Knoflach F
    3. Hernandez MC
    4. Bischofberger J

    (2018) Dendrite-targeting interneurons control synaptic NMD_A-receptor activation vi_a nonlinear α5-GABAA receptors

    Nature Communications 9:3576.

    https://doi.org/10.1038/s41467-018-06004-8

    • PubMed
    • Google Scholar
49. 1. Schulz JM
    2. Knoflach F
    3. Hernandez MC
    4. Bischofberger J

    (2019) Enhanced dendritic inhibition and impaired NMDAR activation in a mouse model of down syndrome

    The Journal of Neuroscience 39:5210–5221.

    https://doi.org/10.1523/JNEUROSCI.2723-18.2019

    • PubMed
    • Google Scholar
50. 1. Serwanski DR
    2. Miralles CP
    3. Christie SB
    4. Mehta AK
    5. Li X
    6. De Blas AL

    (2006) Synaptic and nonsynaptic localization of GABAA receptors containing the alpha5 subunit in the rat brain

    The Journal of Comparative Neurology 499:458–470.

    https://doi.org/10.1002/cne.21115

    • PubMed
    • Google Scholar
51. 1. Silberberg G
    2. Markram H

    (2007) Disynaptic inhibition between neocortical pyramidal cells mediated by martinotti cells

    Neuron 53:735–746.

    https://doi.org/10.1016/j.neuron.2007.02.012

    • PubMed
    • Google Scholar
52. 1. Sohal VS
    2. Zhang F
    3. Yizhar O
    4. Deisseroth K

    (2009) Parvalbumin neurons and gamma rhythms enhance cortical circuit performance

    Nature 459:698–702.

    https://doi.org/10.1038/nature07991

    • PubMed
    • Google Scholar
53. 1. Sternfeld F
    2. Carling RW
    3. Jelley RA
    4. Ladduwahetty T
    5. Merchant KJ
    6. Moore KW
    7. Reeve AJ
    8. Street LJ
    9. O'Connor D
    10. Sohal B
    11. Atack JR
    12. Cook S
    13. Seabrook G
    14. Wafford K
    15. Tattersall FD
    16. Collinson N
    17. Dawson GR
    18. Castro JL
    19. MacLeod AM

    (2004) Selective, orally active gamma-aminobutyric acidA alpha5 receptor inverse agonists as cognition enhancers

    Journal of Medicinal Chemistry 47:2176–2179.

    https://doi.org/10.1021/jm031076j

    • PubMed
    • Google Scholar
54. 1. Tremblay R
    2. Lee S
    3. Rudy B

    (2016) GABAergic interneurons in the neocortex: from cellular properties to circuits

    Neuron 91:260–292.

    https://doi.org/10.1016/j.neuron.2016.06.033

    • PubMed
    • Google Scholar
55. 1. Valbuena S
    2. García Á
    3. Mazier W
    4. Paternain AV
    5. Lerma J

    (2019) Unbalanced dendritic inhibition of CA1 neurons drives spatial-memory deficits in the Ts2Cje down syndrome model

    Nature Communications 10:4991.

    https://doi.org/10.1038/s41467-019-13004-9

    • PubMed
    • Google Scholar
56. 1. Veit J
    2. Hakim R
    3. Jadi MP
    4. Sejnowski TJ
    5. Adesnik H

    (2017) Cortical gamma band synchronization through somatostatin interneurons

    Nature Neuroscience 20:951–959.

    https://doi.org/10.1038/nn.4562

    • PubMed
    • Google Scholar
57. 1. Vinck M
    2. van Wingerden M
    3. Womelsdorf T
    4. Fries P
    5. Pennartz CM

    (2010) The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization

    NeuroImage 51:112–122.

    https://doi.org/10.1016/j.neuroimage.2010.01.073

    • PubMed
    • Google Scholar
58. 1. Wiseman FK
    2. Al-Janabi T
    3. Hardy J
    4. Karmiloff-Smith A
    5. Nizetic D
    6. Tybulewicz VL
    7. Fisher EM
    8. Strydom A

    (2015) A genetic cause of alzheimer disease: mechanistic insights from down syndrome

    Nature Reviews Neuroscience 16:564–574.

    https://doi.org/10.1038/nrn3983

    • PubMed
    • Google Scholar
59. 1. Zorrilla de San Martin J
    2. Delabar JM
    3. Bacci A
    4. Potier MC

    (2018) GABAergic over-inhibition, a promising hypothesis for cognitive deficits in down syndrome

    Free Radical Biology and Medicine 114:33–39.

    https://doi.org/10.1016/j.freeradbiomed.2017.10.002

    • PubMed
    • Google Scholar

Author details

1. Javier Zorrilla de San Martin

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   javier.zorrilla@icm-institute.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2848-7482

2. Cristina Donato

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Present address

   Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg

   Contribution

   Formal analysis, Investigation, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4078-0745

3. Jérémy Peixoto

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Present address

   Institut Pasteur, Paris, France

   Contribution

   Formal analysis, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6814-9747

4. Andrea Aguirre

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1176-8747

5. Vikash Choudhary

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Software, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Angela Michela De Stasi

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Joana Lourenço

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5550-9291

8. Marie-Claude Potier

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   marie-claude.potier@upmc.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2462-7150

9. Alberto Bacci

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   alberto.bacci@icm-institute.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3355-5892

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