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Response to Deep Brain Stimulation in Three Brain Targets with Implications in Mental Disorders: A PET Study in Rats

  • Marta Casquero-Veiga,

    Affiliations CIBER de Salud Mental (CIBERSAM), Madrid, Spain, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain

  • Ravit Hadar,

    Affiliation Department of Psychiatry and Psychotherapy, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

  • Javier Pascau,

    Affiliations CIBER de Salud Mental (CIBERSAM), Madrid, Spain, Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain

  • Christine Winter,

    Affiliation Department of Psychiatry and Psychotherapy, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

  • Manuel Desco ,

    desco@hggm.es

    Affiliations CIBER de Salud Mental (CIBERSAM), Madrid, Spain, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain, Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain

  • María Luisa Soto-Montenegro

    Affiliations CIBER de Salud Mental (CIBERSAM), Madrid, Spain, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain

Abstract

Objective

To investigate metabolic changes in brain networks by deep brain stimulation (DBS) of the medial prefrontal cortex (mPFC), nucleus accumbens (NAcc) and dorsomedial thalamus (DM) using positron emission tomography (PET) in naïve rats.

Methods

43 male Wistar rats underwent stereotactic surgery and concentric bipolar platinum-iridium electrodes were bilaterally implanted into one of the three brain sites. [18F]-fluoro-2-deoxy-glucose-PET (18FDG-PET) and computed tomography (CT) scans were performed at the 7th (without DBS) and 9th day (with DBS) after surgery. Stimulation period matched tracer uptake period. Images were acquired with a small-animal PET-CT scanner. Differences in glucose uptake between groups were assessed with Statistical Parametric Mapping.

Results

DBS induced site-specific metabolic changes, although a common increased metabolic activity in the piriform cortex was found for the three brain targets. mPFC-DBS increased metabolic activity in the striatum, temporal and amygdala, and reduced it in the cerebellum, brainstem (BS) and periaqueductal gray matter (PAG). NAcc-DBS increased metabolic activity in the subiculum and olfactory bulb, and decreased it in the BS, PAG, septum and hypothalamus. DM-DBS increased metabolic activity in the striatum, NAcc and thalamus and decreased it in the temporal and cingulate cortex.

Conclusions

DBS induced significant changes in 18FDG uptake in brain regions associated with the basal ganglia-thalamo-cortical circuitry. Stimulation of mPFC, NAcc and DM induced different patterns of 18FDG uptake despite interacting with the same circuitries. This may have important implications to DBS research suggesting individualized target selection according to specific neural modulatory requirements.

Introduction

Mental disorders are the third leading cause of disability-adjusted life years (DALYs) loss and the first cause of years lived with disability (YLD) in Europe, accounting for 36.1% of those attributable to all causes[1]. Mental disorders greatly influence patients’ overall health, economic situation and social integration. Even though effective treatment exist, 10–30% of the patients have little or no response to traditional treatment strategies and up to an additional 30% of the patients experience only partial relief [2], thus making it essential to explore other treatments. During the last decades, brain electrical stimulation techniques have emerged in the bio-scientific scenario. Among them, deep brain stimulation (DBS) constitutes a neurosurgery technique that modifies neural activity by means of an electrical current applied directly to specific brain targets. It has been licensed as a treatment option for several movement disorders [3]. The idea to extend DBS to the treatment of psychiatric disorders was based on the notion that psychiatric disorders are the clinical presentation of dysfunctional brain networks and the observation that DBS induces depressive and hypomanic states in Parkinson’s disease patients [4]. Meanwhile, DBS in the ventral capsule/ventral striatum (VC/VS), which contains the nucleus accumbens (NAcc), has received FDA approval for treatment of obsessive compulsive disorders, is being tested for treatment of depressive disorders [57] and addiction [811] and the first preclinical report on successful DBS in the context of schizophrenia has just been published [12]. The only double-blind sham-controlled trials for chronic treatment-resistant depression stimulated the VC/VS[13] and Brodmann area 25[14], obtaining little success. Thus, it is noteworthy that, with exception of VC/VS-DBS for OCD, there is no much evidence yet supporting open loop DBS for psychiatric indications. Future research applying new study designs and DBS parameters (e.g. close-loop DBS[15]) are needed to confirm its clinical potential. On the other hand, DBS also holds scientific promise in the identification of interconnected functional networks and dysfunctional brain circuits underlying a physiological and pathological brain functions due to its capacity to specifically modify neural discharge patterns locally, at the electrode placement, and remotely, in associated brain areas [16, 17] and affect neural network activity[1820]. Across the neuro-psychiatric disorders currently subjected to DBS treatment trials, the following DBS targets are being tested: medial prefrontal cortex (mPFC), globus pallidus internus, subthalamic nucleus, zona incerta, nucleus accumbens (NAcc)/ventral striatum, hippocampus and thalamus (centromedian/parafascicularis; anterior nucleus; periaqueductal gray/periventricular gray; ventrolateral intermedius; ventral posterolateral/ventro-posteromedial), lateral habenula, nucleus basali Meynert, medial forebrain bundle (MFB), and fornix/hypothalamus [2124]. In addition, the mediodorsal thalamic nucleus (DM) structure has been suggested relevant in the context of psychiatric disorders as it interconnects with the dorsolateral PFC and limbic structures, including limbic cortex, hippocampus and basolateral amygdala [25]. Nevertheless, there is no consensus on which area is best for each disorder. Indeed, several areas are being investigated for the same pathology, i.e. mPFC, cingulum, MFB, ventral striatum or the NAcc for depression, STN, NAcc or ventral striatum for obsessive compulsive, mPFC or NAcc for future schizophrenia studies; more to that, some cases, the same area is being investigated for several disorders [2629]. So far, targets have been selected upon assumptions about the pathophysiological relevance of the respective brain site in the manifestation of the respective disorder but often enough lack a scientific framework that proves the selection. From a theoretical point of view, the optimal DBS target would be the one that mostly interconnects with circuits involved in the manifestation of the symptoms to be targeted.

In this context, functional neuroimaging is a powerful tool in terms of locating brain networks modulated by DBS and refining stimulation protocols [30]. Positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose (18FDG) constitutes the traditional technique for in vivo direct quantification of regional brain glucose metabolism in humans and rodents [12, 18, 3135]. The method has proven itself as an excellent tool for promoting our understanding of the neurobiological processes in healthy as well as diseased brains and allows for reliable comparative studies [3640]. We used here 18FDG-PET and statistical parametric mapping (SPM) techniques in rats to compare the metabolic modulation of neural networks by DBS applied to either the mPFC, NAcc or DM, all of which are linked to several known neuropsychiatric disorders [4144].

Materials and Methods

Animals

Forty-three male Wistar rats (275–325 g) were housed in a temperature (24 ± 0.5°C) and humidity controlled vivarium with a 12 h light-dark cycle. Commercial rodent laboratory chow and water were available ad libitum if not indicated differently. All experimental animal procedures were conducted according to the European Communities Council Directive 2010/63/EU and approved by the Ethics Committee for Animal Experimentation of our hospital (Comité de Ética de Experimentación Animal, CEEA; number ES280790000087).

Surgery and DBS protocol

Stereotaxic surgeries were carried out on animals anesthetized with a mixture of ketamine (100 mg kg-1) and xylazine (10 mg kg-1). Concentric bipolar platinum-iridium electrodes (Plastics One, Roanoke, USA) were bilaterally implanted in one of the following targets, according to the Paxinos and Watson rat brain atlas [45]: 1) mPFC; anteroposterior (AP) +3.5 (from Bregma), medio-lateral (ML) +0.6, dorso-ventral (DV) -3.4 (from Dura); 2) NAcc: AP +1.2, ML +1.8, DV -8.1; and 3) DM: AP -2.8, ML +0.75, DV -5.0. Electrodes were fixed to the skull with dental acrylic cement (Technovit®). Computed tomography (CT) scans of all the animals were obtained and co-registered to an MRI study of one non-operated animal (anatomical MRI template) to rule out errors in the placement of the electrodes. Only animals with correct electrodes positions were included in the PET study resulting in the following number of animals per group: 1) mPFC: 10, 2) NAcc: 10 and 3) DM: 11.

PET scans were acquired seven and nine days thereafter, preceded by either sham stimulation (baseline-condition) or DBS applied during 18FDG-uptake period (DBS-condition) for 45 minutes. DBS was performed with an isolated stimulator (STG1004; Multi Channel Systems GmbH, Reutlingen, Germany) in a constant current mode at 130 Hz and 150 μA with a pulse width of 100 μs. These settings were chosen based on previous studies by our group [18, 19].

Imaging studies

All animals were scanned using a small-animal PET/CT scanner (ARGUS PET/CT, SEDECAL, Madrid) under anesthesia with isoflurane (3% induction and 1.5% maintenance in 100% O2). 18FDG (approximately 1 mCi) was injected into the tail vein and, after an uptake period of 45 minutes, animals were scanned for 45 minutes. Images were reconstructed using a 2D-OSEM (ordered subset expectation maximization) algorithm, which claims a spatial resolution for this scanner of 1.45 mm FWHM (full width at half maximum), with a voxel size of 0.3875 x 0.3875 x 0.7750 mm3. The energy window was 400–700 keV. Decay and deadtime corrections were applied.

CT studies were acquired with the following parameters: 340 mA, 40 KV, 360 projections, 8 shots per projection, and 200 μm of resolution. CT images were reconstructed using a Feldkamp algorithm (isotropic voxel size of 0.121 mm).

In addition, one MRI scan of a non-operated animal was acquired with a 7-Tesla Biospec 70/20 scanner (Bruker, Ettlingen, Germany) under sevoflurane anesthesia (4.5% for induction and 2.5% for maintenance in 100% O2,). A T2-weighted spin echo sequence was acquired, with TE = 33 ms, TR = 3732 ms, and a slice thickness of 0.8 mm (34 slices). The matrix size was 256 × 256 pixels at an FOV of 3.5 × 3.5 cm2. This single-animal study was used as an anatomical template in order to display the results of the SPM study.

Analysis of PET data

CT studies were co-registered to a random reference CT scan using an automatic rigid registration method based on mutual information, and the spatial transformation obtained for each CT image was subsequently applied to the corresponding PET[46]. The single MRI study was also spatially co-registered to the reference CT scan. A brain mask segmented on the MRI study was applied to all registered PET images and the resulting images were smoothed with an isotropic Gaussian filter (2 mm FWHM). Voxel values were normalized to the average white matter intensity in order to obtain the regional characterization of metabolic changes circumventing overall differences in animal brain metabolism. White matter normalization was used in accordance with the criteria of Shinohara et al.[47].

A region of interest (ROI) analysis was performed to determine the global metabolic differences. Whole brain and white matter masks segmented on the MR template were used for this analysis. Whole brain data were normalized to average white matter intensity.

Statistical analysis

Statistical analysis of regional PET data was performed using the software package SPM12 (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging, London, UK). Groups were compared by means of a paired t test with a significance threshold of p<0.01 (T = 2.82), uncorrected for multiple comparisons. To reduce type I error, a 50-voxel clustering threshold (spatial-extent) was applied. Global differences were assessed by means of a paired t-test with a threshold for statistical significance set at p<0.01.

Results

Fig 1 shows sagittal, coronal and axial views of a CT scan registered to the MR template of one animal to verify the correct electrode positioning. Only animals with electrodes placed correctly in the respective target were included in the study.

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Fig 1. Correct electrode location verification.

Sagittal, coronal and axial views of a CT scan registered to the MR template of an animal to verify the correct electrode location. Only animals with electrodes placed correctly in the respective target were included in the study.

https://doi.org/10.1371/journal.pone.0168689.g001

Measurements based on global differences for the whole brain metabolism displayed no significant differences across groups under either treatment, sham-stimulation or DBS. Values for DBS animals were normalized and expressed as a ratio of the average glucose metabolism in the basal time point for each animal: mPFC (0.99±0.020) (p = 0.099), NAcc (1.03±0.15) (p = 0.631) and DM (0.99±0.032) (p = 0.185).

mPFC-DBS treatment increased metabolic activity in the striatum, temporal and piriform cortex and amygdala (right: T = 6.39, p<0.001; left: T = 4.98, p<0.001), and reduced it in the cerebellum, brainstem and periaqueductal gray matter (T = 11.52, p<0.001) (Fig 2, Table 1).

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Fig 2. Brain glucose metabolism during DBS in the three brain targets. Effects depend on stimulation target.

Colored PET overlays on MR reference indicate increased 18FDG uptake (hot colors) or decreased (cold colors). AA: amygdala; BS: brainstem, Cb: cerebellum, CC: cingulate cortex, Hypoth: hypothalamus, NAcc: nucleus accumbens, PAG: periaqueductal gray matter, Pir C: piriform cortex, Sub: subiculum hippocampal, Str: striatum, Temp C: temporal cortex, Th: thalamus.

https://doi.org/10.1371/journal.pone.0168689.g002

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Table 1. Changes in brain metabolic activity during DBS in the three brain targets.

https://doi.org/10.1371/journal.pone.0168689.t001

NAcc-DBS treatment increased metabolic activity in the left subiculum (T = 13.02, p<0.001), piriform cortex (right: T = 4.29, p = 0.001; left: T = 6.52, p<0.001) and olfactory bulb (T = 5.20, p<0.001), and decreased 18FDG-uptake in the brainstem and PAG (T = 4.82, p = 0.001), septum (T = 5.27, p<0.001) and hypothalamus (T = 3.25, p = 0.005) (Fig 2, Table 1).

DM-DBS treatment increased metabolic activity in the striatum, NAcc and piriform cortex (right: T = 7.25, p<0.001; left: T = 3.73, p = 0.002) and thalamus (T = 7.78, p<0.001) and decreased 18FDG-uptake in the temporal (right: T = 3.43, p = 0.003; left: T = 4.58, p = 0.001) and cingulate cortex (T = 3.64, p = 0.001) (Fig 2, Table 1).

Discussion

To the best of our knowledge, this is the first comparative report on the use of small animal 18FDG-PET and SPM techniques in rats in an attempt to identify and compare the modulation of brain metabolic networks by DBS in the mPFC, NAcc and DM. We show that the effects of high frequency DBS on neuronal activity, reflected as the differences in regional glucose metabolism between DBS on and off conditions, involve modifications of complex networks rather than global or isolated regions. This is in agreement with our previous study for mPFC and NAcc stimulation in an animal model of schizophrenia [12]. Its capability to either increase or decrease activity supports the notion that DBS induces several mechanisms that lead to net inhibitory and excitatory effects irrespective of the function [48], suggesting a complex modulation of activity along cortico-basal ganglia-thalamo-cortical and the cerebello-thalamo-cortical circuits. Overall, stimulation in each brain target influenced a different set of structures at a distance from the target that might be relevant for addressing specific pathological conditions.

Common DBS effects across different targets

DBS to all three targets induced increased metabolic activity in the piriform cortex (PC). The PC is the largest area of the mammalian olfactory cortex, receives direct projections from the olfactory bulb and contains the most susceptible neural circuits of all forebrain regions for electrical (or chemical) stimulation [49, 50]. Thus, immunohistochemical studies have shown that during electrical stimulation of limbic brain regions, the PC exhibits the most consistent increase in glucose utilization [49], similar to our results.

Another interesting finding is that both mPFC-DBS and NAcc-DBS decreased glucose metabolism in the brainstem. The mPFC is reciprocally connected with the dorsal raphe nucleus, which contains most ascending serotonergic neurons, and the ventral tegmental area (VTA) which contains mesocortical dopaminergic (DA) neurons, which could account for the decreased glucose metabolism seen in the brainstem. The medium spiny neurons of NAcc receive input from both dopaminergic neurons in the VTA and the glutamatergic neurons of the hippocampus, amygdala and mPFC. Thus, stimulation of NAcc at high frequencies could lead to an inhibition of dopaminergic activity at the brainstem level, resulting in decreased glucose metabolism in the brainstem. Our results are in line with that reported with citalopram, an antidepressant medication, showing decreased blood oxygenation level dependent (BOLD) signal in the brainstem using pharmacological magnetic resonance imaging [51]. In this sense, both brain targets have been recently proposed as targets for DBS in resistant major depressive disorder [52, 53], and has been associated with antidepressant, anxiolytic, and precognitive properties.

mPFC-DBS increased brain metabolism in the temporal cortices

Hypofrontality is related to deficits in attention, memory and executive function, apathy, social withdrawal, restricted affection or anhedonia [54]. It has been suggested that the direct stimulation of the PFC may serve to modulate temporo-parietal attentional networks involved in the automatic processing of salient stimuli [30], playing a critical role in mood regulation [55]. In this sense, cortical stimulation for treatment-resistant depression constitutes a brain stimulation approach that has shown promise [5658]. Here, we show that mPFC-DBS affected metabolic activity in the striatum, temporal and piriform cortices, the amygdala, cerebellum, brainstem and periaqueductal gray matter. This is in line with the PFC projecting to the ventral striatum and the head of the caudate, as well as other subcortical connections, including the amygdala [59]. Thus, our results showing an increased metabolism in temporal cortices support the notion that stimulation of mPFC could be explored for improving the attentional network. Moreover, behavioral experiments should be performed to corroborate these findings.

Cerebellar affectation has been commonly reported in schizophrenia, autism, and other developmental disorders [6062]. Recent neuroanatomical evidence has also demonstrated closed-loop connectivity between prefrontal cortex and the cerebellum [63]. Moreover, electrophysiological and anatomical studies have demonstrated the existence of a prefrontal-olivo-cerebellar pathway in anesthetized mice [60], and the existence of disynaptic fronto-cerebellar connectivity in rats [64]. Our data showing that mPFC-DBS decreased glucose metabolism in the cerebellum, confirm the existence of a rodent prefrontal-cerebellar network [65, 66].

NAcc-DBS increased brain metabolism in the subiculum

The NAcc has traditionally been associated with reward, pleasure and addiction, behavioral categories/systems implicated in the pathophysiology of basically all psychiatric disorders [16, 6769]. In fact, the ventral capsule/ventral striatum (VC/VS), which includes the NAcc, is the unique brain target with FDA approval for DBS treatment of a psychiatric condition (OCD). The NAcc receives major dopaminergic afferents from mesolimbic origin, and dopamine is the most important transmitter within these nuclei. Thus, NAcc stimulation may lead to direct interferences in the dopaminergic system, or possibly indirect influences on the synaptic efficiency of this neurotransmitter system, with a huge spread of metabolic changes in the brain. Given its vast pathophysiological implication, network effects of NAcc-DBS were less striking and limited to the subiculum, piriform cortex (PC), olfactory bulb (OB), and brainstem. Off note, findings basically correspond to NAcc-DBS we recently reported using a functional MRI approach [70]. Of those effects, the increase of glucose metabolism in the subiculum is of particular interest. Neuroimaging and neuropsychological studies have shown an hippocampal dysfunction in Alzheimer's disease, cognitive ageing, post-traumatic stress disorder, obesity, schizophrenia, and depressive and anxiety disorders, among others [71]. Specifically in schizophrenia, there is robust evidence of hippocampal dysfunction, with impaired activation during memory tasks, increased baseline hippocampal perfusion, and reduced dentate gyrus neurogenesis and efferent signaling [72]. Moreover, obesity has been associated with defective hippocampal activity, which leads to cognitive deficiency in obese patients [73]. In this context and according to our results, it seems reasonable to explore the idea of applying NAcc-DBS in pathologies associated with hippocampal dysfunction.

DM-DBS increased brain metabolism in the thalamus

The dorsomedial thalamus (DM) has strong interconnections with the dorsolateral PFC and limbic structures, besides being a critical element in the attentional “selective engagement” system. The dysfunction of this “sensory gating apparatus” has been associated to hallucinations, a common symptom in psychosis [74, 75]. At present, DM-DBS has only been applied experimentally in animal models [21, 53, 7679]. Here, we found that DM-DBS affected metabolic activity in the striatum, NAcc, piriform cortex, medial thalamus and temporal and cingulate cortices. This is in line with the DM projections to the dorsolateral prefrontal and orbitofrontal cortical areas, which together project to the anterior cingulate cortex [80] and to the dorsal and ventral striatum [81]. Among those effects, the increase of glucose metabolism in the thalamus is especially relevant from a translational point of view. Neuroimaging has shown abnormalities in the DM of schizophrenic patients, with decreases in the thalamic D2 receptor binding [82], less prominent thalamic glucose metabolism rate [83], decrease functional connectivity of DM to other circuit areas or decreases in the thalamic blood flow [84]. Patients with frontotemporal lobe degeneration associated with dementia also shown decreased glucose metabolism in the medial temporal region, the thalamus and striatum [85]. In Alzheimer disease, thalamic abnormalities at the anterior thalamic nuclei have been associated with cognitive deficits in memory and attention [86]. In view of these studies and our results, it seems essential to explore the idea of applying DM-DBS in pathologies associated with cognitive deficits in memory and attention and dementias.

Limitations of the study

Our study is subject to two limiting factors. The first is the use of naïve animals to study DBS’ effects. Clearly, in the clinic, DBS is applied to diseased brains and its therapeutic effects are a function of its interaction with altered brain network. Another limitation is related to the temporal influence of DBS as studied here; we used an acute stimulation protocol preceding the PET scans acquisition. In the clinical scenario, DBS is applied chronically and usually therapeutic effects evolve over a timeline of stimulation.

Conclusion

In conclusion, we show that DBS in mPFC, NAcc and DM induced different patterns of 18FDG uptake despite sharing interconnections with the same circuitry, and this may have important implications to DBS research suggesting individualized target selection according to specific neural modulatory requirements.

Acknowledgments

We thank Alexandra de Francisco and Yolanda Sierra for their support in stereotactic surgery and animal handling.

Author Contributions

  1. Conceptualization: MLSM CW JP MD.
  2. Formal analysis: MCV JP MLSM MD.
  3. Funding acquisition: CW MD MLSM.
  4. Methodology: MCV RH MLSM.
  5. Resources: MLSM JP MD.
  6. Writing – original draft: MCV RH JP CW MD MLSM.
  7. Writing – review & editing: MCV RH JP CW MD MLSM.

References

  1. 1. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2163–96. pmid:23245607
  2. 2. Burns JK, Tomita A, Kapadia AS. Income inequality and schizophrenia: increased schizophrenia incidence in countries with high levels of income inequality. The International journal of social psychiatry. 2014;60(2):185–96. pmid:23594564
  3. 3. Benabid AL, Chabardes S, Mitrofanis J, Pollak P. Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease. The Lancet Neurology. 2009;8(1):67–81. pmid:19081516
  4. 4. Bejjani BP, Damier P, Arnulf I, Thivard L, Bonnet AM, Dormont D, et al. Transient acute depression induced by high-frequency deep-brain stimulation. The New England journal of medicine. 1999;340(19):1476–80. pmid:10320386
  5. 5. Williams NR, Hopkins TR, Short EB, Sahlem GL, Snipes J, Revuelta GJ, et al. Reward circuit DBS improves Parkinson's gait along with severe depression and OCD. Neurocase. 2016;22(2):201–4. pmid:26644268
  6. 6. Berlim MT, McGirr A, Van den Eynde F, Fleck MP, Giacobbe P. Effectiveness and acceptability of deep brain stimulation (DBS) of the subgenual cingulate cortex for treatment-resistant depression: a systematic review and exploratory meta-analysis. J Affect Disord. 2014;159:31–8. pmid:24679386
  7. 7. Strutt AM, Simpson R, Jankovic J, York MK. Changes in cognitive-emotional and physiological symptoms of depression following STN-DBS for the treatment of Parkinson's disease. European journal of neurology. 2012;19(1):121–7. pmid:21668586
  8. 8. Batra V, Tran TL, Caputo J, Guerin GF, Goeders NE, Wilden J. Intermittent bilateral deep brain stimulation of the nucleus accumbens shell reduces intravenous methamphetamine intake and seeking in Wistar rats. Journal of neurosurgery. 2016:1–12.
  9. 9. Muller UJ, Sturm V, Voges J, Heinze HJ, Galazky I, Buntjen L, et al. Nucleus Accumbens Deep Brain Stimulation for Alcohol Addiction—Safety and Clinical Long-term Results of a Pilot Trial. Pharmacopsychiatry. 2016;49(4):170–3. pmid:27145161
  10. 10. Muller UJ, Voges J, Steiner J, Galazky I, Heinze HJ, Moller M, et al. Deep brain stimulation of the nucleus accumbens for the treatment of addiction. Annals of the New York Academy of Sciences. 2013;1282:119–28. pmid:23227826
  11. 11. Creed M, Pascoli VJ, Luscher C. Addiction therapy. Refining deep brain stimulation to emulate optogenetic treatment of synaptic pathology. Science. 2015;347(6222):659–64. pmid:25657248
  12. 12. Bikovsky L, Hadar R, Soto-Montenegro ML, Klein J, Weiner I, Desco M, et al. Deep brain stimulation improves behavior and modulates neural circuits in a rodent model of schizophrenia. Exp Neurol. 2016;283(Pt A):142–50. pmid:27302677
  13. 13. Dougherty DD, Rezai AR, Carpenter LL, Howland RH, Bhati MT, O'Reardon JP, et al. A Randomized Sham-Controlled Trial of Deep Brain Stimulation of the Ventral Capsule/Ventral Striatum for Chronic Treatment-Resistant Depression. Biol Psychiatry. 2015;78(4):240–8. pmid:25726497
  14. 14. Morishita T, Fayad SM, Higuchi MA, Nestor KA, Foote KD. Deep brain stimulation for treatment-resistant depression: systematic review of clinical outcomes. Neurotherapeutics. 2014;11(3):475–84. pmid:24867326
  15. 15. Widge AS, Ellard KK, Paulk AC, Basu I, Yousefi A, Zorowitz S, et al. Treating refractory mental illness with closed-loop brain stimulation: Progress towards a patient-specific transdiagnostic approach. Exp Neurol. 2016.
  16. 16. Kuhn J, Bodatsch M, Sturm V, Lenartz D, Klosterkotter J, Uhlhaas PJ, et al. [Deep brain stimulation in schizophrenia]. Fortschritte der Neurologie-Psychiatrie. 2011;79(11):632–41. pmid:22048856
  17. 17. Kuhn J, Grundler TO, Lenartz D, Sturm V, Klosterkotter J, Huff W. Deep brain stimulation for psychiatric disorders. Deutsches Arzteblatt international. 2010;107(7):105–13. pmid:20221269
  18. 18. Klein J, Soto-Montenegro ML, Pascau J, Gunther L, Kupsch A, Desco M, et al. A novel approach to investigate neuronal network activity patterns affected by deep brain stimulation in rats. J Psychiatr Res. 2011;45(7):927–30. Epub 2011/01/14. pmid:21227451
  19. 19. Soto-Montenegro ML, Pascau J, Desco M. Response to Deep Brain Stimulation in the Lateral Hypothalamic Area in a Rat Model of Obesity: In Vivo Assessment of Brain Glucose Metabolism. Mol Imaging Biol. 2014. Epub 2014/06/07.
  20. 20. Van Den Berge N, Keereman V, Vanhove C, Van Nieuwenhuyse B, van Mierlo P, Raedt R, et al. Hippocampal deep brain stimulation reduces glucose utilization in the healthy rat brain. Mol Imaging Biol. 2015;17(3):373–83. pmid:25361593
  21. 21. Klein J, Hadar R, Gotz T, Manner A, Eberhardt C, Baldassarri J, et al. Mapping brain regions in which deep brain stimulation affects schizophrenia-like behavior in two rat models of schizophrenia. Brain Stimul. 2013;6(4):490–9. Epub 2012/10/23. pmid:23085443
  22. 22. Mikell CB, McKhann GM, Segal S, McGovern RA, Wallenstein MB, Moore H. The hippocampus and nucleus accumbens as potential therapeutic targets for neurosurgical intervention in schizophrenia. Stereotactic and functional neurosurgery. 2009;87(4):256–65. pmid:19556835
  23. 23. Peled A. Optogenetic neuronal control in schizophrenia. Medical hypotheses. 2011;76(6):914–21. pmid:21482453
  24. 24. Hamani C, Nobrega JN. Deep brain stimulation in clinical trials and animal models of depression. Eur J Neurosci. 2010;32(7):1109–17. pmid:21039950
  25. 25. Mitchell AS, Chakraborty S. What does the mediodorsal thalamus do? Frontiers in systems neuroscience. 2013;7:37. pmid:23950738
  26. 26. Hamani C, Nobrega JN, Lozano AM. Deep brain stimulation in clinical practice and in animal models. Clinical pharmacology and therapeutics. 2010;88(4):559–62. pmid:20720537
  27. 27. Hamani C, Temel Y. Deep brain stimulation for psychiatric disease: contributions and validity of animal models. Sci Transl Med. 2012;4(142):142rv8. Epub 2012/07/13. pmid:22786683
  28. 28. Lyons MK. Deep brain stimulation: current and future clinical applications. Mayo Clinic proceedings. 2011;86(7):662–72. pmid:21646303
  29. 29. Salgado-Lopez L, Pomarol-Clotet E, Roldan A, Rodriguez R, Molet J, Sarro S, et al. Letter to the Editor: Deep brain stimulation for schizophrenia. Journal of neurosurgery. 2016;125(1):229–30. pmid:27104842
  30. 30. Williams NR, Taylor JJ, Lamb K, Hanlon CA, Short EB, George MS. Role of functional imaging in the development and refinement of invasive neuromodulation for psychiatric disorders. World journal of radiology. 2014;6(10):756–78. pmid:25349661
  31. 31. Akdemir UO, Tokcaer AB, Karakus A, Kapucu LO. Brain 18F-FDG PET imaging in the differential diagnosis of parkinsonism. Clinical nuclear medicine. 2014;39(3):e220–6. pmid:24321825
  32. 32. Gallivanone F, Della Rosa PA, Castiglioni I. Statistical Voxel-Based Methods and [18F]FDG PET Brain Imaging: Frontiers for the Diagnosis of AD. Current Alzheimer research. 2016;13(6):682–94. pmid:26567733
  33. 33. Kato T, Inui Y, Nakamura A, Ito K. Brain fluorodeoxyglucose (FDG) PET in dementia. Ageing research reviews. 2016.
  34. 34. Soto-Montenegro ML, Pascau J, Desco M. Response to deep brain stimulation in the lateral hypothalamic area in a rat model of obesity: in vivo assessment of brain glucose metabolism. Mol Imaging Biol. 2014;16(6):830–7. Epub 2014/06/07. pmid:24903031
  35. 35. Van Weehaeghe D, Ceccarini J, Delva A, Robberecht W, Van Damme P, Van Laere K. Prospective Validation of 18F-FDG Brain PET Discriminant Analysis Methods in the Diagnosis of Amyotrophic Lateral Sclerosis. J Nucl Med. 2016.
  36. 36. Song IU, Choi EK, Oh JK, Chung YA, Chung SW. Alteration patterns of brain glucose metabolism: comparisons of healthy controls, subjective memory impairment and mild cognitive impairment. Acta radiologica. 2016;57(1):90–7. pmid:25538106
  37. 37. Qiu X, Zhang Y, Feng H, Jiang D. Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients. Frontiers in neuroscience. 2016;10:235. pmid:27303259
  38. 38. Moghbel M, Newberg A, Alavi A. Positron emission tomography: ligand imaging. Handbook of clinical neurology. 2016;135:229–40. pmid:27432668
  39. 39. Trotta N, Archambaud F, Goldman S, Baete K, Van Laere K, Wens V, et al. Functional integration changes in regional brain glucose metabolism from childhood to adulthood. Human brain mapping. 2016;37(8):3017–30. pmid:27133021
  40. 40. Segobin S, La Joie R, Ritz L, Beaunieux H, Desgranges B, Chetelat G, et al. FDG-PET Contributions to the Pathophysiology of Memory Impairment. Neuropsychology review. 2015;25(3):326–55. pmid:26319237
  41. 41. Marenco S, Stein JL, Savostyanova AA, Sambataro F, Tan HY, Goldman AL, et al. Investigation of anatomical thalamo-cortical connectivity and FMRI activation in schizophrenia. Neuropsychopharmacology. 2012;37(2):499–507. pmid:21956440
  42. 42. Drevets WC, Raichle ME. Neuroanatomical circuits in depression: implications for treatment mechanisms. Psychopharmacology bulletin. 1992;28(3):261–74. pmid:1480730
  43. 43. Lui S, Wu Q, Qiu L, Yang X, Kuang W, Chan RC, et al. Resting-state functional connectivity in treatment-resistant depression. Am J Psychiatry. 2011;168(6):642–8. pmid:21362744
  44. 44. Lapidus KA, Stern ER, Berlin HA, Goodman WK. Neuromodulation for obsessive-compulsive disorder. Neurotherapeutics: the journal of the American Society for Experimental NeuroTherapeutics. 2014;11(3):485–95.
  45. 45. Paxinos G, Watson C. The rat brain in stereotaxic coordinates. 4th ed. San Diego: Academic Press 2008.
  46. 46. Pascau J, Gispert JD, Michaelides M, Thanos PK, Volkow ND, Vaquero JJ, et al. Automated method for small-animal PET image registration with intrinsic validation. Mol Imaging Biol. 2009;11(2):107–13. Epub 2008/08/02. pmid:18670824
  47. 47. Shinohara RT, Sweeney EM, Goldsmith J, Shiee N, Mateen FJ, Calabresi PA, et al. Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin. 2014;6:9–19. Epub 2014/11/08. pmid:25379412
  48. 48. McIntyre CC, Savasta M, Kerkerian-Le Goff L, Vitek JL. Uncovering the mechanism(s) of action of deep brain stimulation: activation, inhibition, or both. Clin Neurophysiol. 2004;115(6):1239–48. Epub 2004/05/12. pmid:15134690
  49. 49. Boix-Trelis N, Vale-Martinez A, Guillazo-Blanch G, Marti-Nicolovius M. Induction of c-Fos expression by electrical stimulation of the nucleus basalis magnocellularis. Neurosci Lett. 2009;449(2):137–41. Epub 2008/11/18. pmid:19013218
  50. 50. Loscher W, Ebert U, Lehmann H. Kindling induces a lasting, regionally selective increase of kynurenic acid in the nucleus accumbens. Brain Res. 1996;725(2):252–6. pmid:8836532
  51. 51. Sekar S, Verhoye M, Van Audekerke J, Vanhoutte G, Lowe AS, Blamire AM, et al. Neuroadaptive responses to citalopram in rats using pharmacological magnetic resonance imaging. Psychopharmacology (Berl). 2011;213(2–3):521–31.
  52. 52. Millet B, Jaafari N, Polosan M, Baup N, Giordana B, Haegelen C, et al. Limbic versus cognitive target for deep brain stimulation in treatment-resistant depression: accumbens more promising than caudate. Eur Neuropsychopharmacol. 2014;24(8):1229–39. pmid:24950819
  53. 53. Hamani C, Amorim BO, Wheeler AL, Diwan M, Driesslein K, Covolan L, et al. Deep brain stimulation in rats: different targets induce similar antidepressant-like effects but influence different circuits. Neurobiology of disease. 2014;71:205–14. pmid:25131446
  54. 54. Arnsten AF, Rubia K. Neurobiological circuits regulating attention, cognitive control, motivation, and emotion: disruptions in neurodevelopmental psychiatric disorders. Journal of the American Academy of Child and Adolescent Psychiatry. 2012;51(4):356–67. pmid:22449642
  55. 55. Levesque J, Eugene F, Joanette Y, Paquette V, Mensour B, Beaudoin G, et al. Neural circuitry underlying voluntary suppression of sadness. Biol Psychiatry. 2003;53(6):502–10. pmid:12644355
  56. 56. Panov F, Kopell BH. Use of cortical stimulation in neuropathic pain, tinnitus, depression, and movement disorders. Neurotherapeutics: the journal of the American Society for Experimental NeuroTherapeutics. 2014;11(3):564–71.
  57. 57. Hamani C, Diwan M, Macedo CE, Brandao ML, Shumake J, Gonzalez-Lima F, et al. Antidepressant-like effects of medial prefrontal cortex deep brain stimulation in rats. Biol Psychiatry. 2010;67(2):117–24. Epub 2009/10/13. pmid:19819426
  58. 58. Hamani C, Nobrega JN. Preclinical studies modeling deep brain stimulation for depression. Biol Psychiatry. 2012;72(11):916–23. Epub 2012/07/04. pmid:22748616
  59. 59. Klein JC, Rushworth MF, Behrens TE, Mackay CE, de Crespigny AJ, D'Arceuil H, et al. Topography of connections between human prefrontal cortex and mediodorsal thalamus studied with diffusion tractography. Neuroimage. 2010;51(2):555–64. pmid:20206702
  60. 60. Mittleman G, Goldowitz D, Heck DH, Blaha CD. Cerebellar modulation of frontal cortex dopamine efflux in mice: relevance to autism and schizophrenia. Synapse. 2008;62(7):544–50. pmid:18435424
  61. 61. Konarski JZ, McIntyre RS, Grupp LA, Kennedy SH. Is the cerebellum relevant in the circuitry of neuropsychiatric disorders? J Psychiatry Neurosci. 2005;30(3):178–86. pmid:15944742
  62. 62. Rogers TD, McKimm E, Dickson PE, Goldowitz D, Blaha CD, Mittleman G. Is autism a disease of the cerebellum? An integration of clinical and pre-clinical research. Frontiers in systems neuroscience. 2013;7:15. pmid:23717269
  63. 63. Watson TC, Becker N, Apps R, Jones MW. Back to front: cerebellar connections and interactions with the prefrontal cortex. Frontiers in systems neuroscience. 2014;8:4. pmid:24550789
  64. 64. Suzuki L, Coulon P, Sabel-Goedknegt EH, Ruigrok TJ. Organization of cerebral projections to identified cerebellar zones in the posterior cerebellum of the rat. J Neurosci. 2012;32(32):10854–69. pmid:22875920
  65. 65. Bostan AC, Dum RP, Strick PL. Cerebellar networks with the cerebral cortex and basal ganglia. Trends in cognitive sciences. 2013;17(5):241–54. pmid:23579055
  66. 66. Kelly RM, Strick PL. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J Neurosci. 2003;23(23):8432–44. pmid:12968006
  67. 67. Grace AA. Gating of information flow within the limbic system and the pathophysiology of schizophrenia. Brain research Brain research reviews. 2000;31(2–3):330–41. pmid:10719160
  68. 68. Ma J, Leung LS. Deep brain stimulation of the medial septum or nucleus accumbens alleviates psychosis-relevant behavior in ketamine-treated rats. Behav Brain Res. 2014;266:174–82. Epub 2014/03/19. pmid:24632470
  69. 69. Halpern CH, Torres N, Hurtig HI, Wolf JA, Stephen J, Oh MY, et al. Expanding applications of deep brain stimulation: a potential therapeutic role in obesity and addiction management. Acta Neurochir (Wien). 2011;153(12):2293–306. Epub 2011/10/07.
  70. 70. Hadar R, Vengeliene V, Barroeta Hlusicke E, Canals S, Noori HR, Wieske F, et al. Paradoxical augmented relapse in alcohol-dependent rats during deep-brain stimulation in the nucleus accumbens. Transl Psychiatry. 2016;6(6):e840. pmid:27327255
  71. 71. Small SA, Schobel SA, Buxton RB, Witter MP, Barnes CA. A pathophysiological framework of hippocampal dysfunction in ageing and disease. Nature reviews Neuroscience. 2011;12(10):585–601. pmid:21897434
  72. 72. Tamminga CA, Stan AD, Wagner AD. The hippocampal formation in schizophrenia. Am J Psychiatry. 2010;167(10):1178–93. Epub 2010/09/03. pmid:20810471
  73. 73. Mueller K, Sacher J, Arelin K, Holiga S, Kratzsch J, Villringer A, et al. Overweight and obesity are associated with neuronal injury in the human cerebellum and hippocampus in young adults: a combined MRI, serum marker and gene expression study. Transl Psychiatry. 2012;2:e200. Epub 2012/12/06. pmid:23212584
  74. 74. Spalletta G, Piras F, Alex Rubino I, Caltagirone C, Fagioli S. Fronto-thalamic volumetry markers of somatic delusions and hallucinations in schizophrenia. Psychiatry Res. 2013;212(1):54–64. pmid:23158777
  75. 75. Mathew RJ, Duncan GC, Weinman ML, Barr DL. Regional cerebral blood flow in schizophrenia. Arch Gen Psychiatry. 1982;39(10):1121–4. pmid:7125842
  76. 76. Ewing SG, Porr B, Pratt JA. Deep brain stimulation of the mediodorsal thalamic nucleus yields increases in the expression of zif-268 but not c-fos in the frontal cortex. Journal of chemical neuroanatomy. 2013;52:20–4. pmid:23660497
  77. 77. Covolan L, de Almeida AC, Amorim B, Cavarsan C, Miranda MF, Aarao MC, et al. Effects of anterior thalamic nucleus deep brain stimulation in chronic epileptic rats. PLoS One. 2014;9(6):e97618. pmid:24892420
  78. 78. Wang S, Wu DC, Fan XN, Zhu MZ, Hu QY, Zhou D, et al. Mediodorsal thalamic stimulation is not protective against seizures induced by amygdaloid kindling in rats. Neurosci Lett. 2010;481(2):97–101. Epub 2010/07/06. pmid:20600600
  79. 79. Zhong XL, Yu JT, Zhang Q, Wang ND, Tan L. Deep brain stimulation for epilepsy in clinical practice and in animal models. Brain research bulletin. 2011;85(3–4):81–8. pmid:21457763
  80. 80. Groenewegen HJ, Galis-de Graaf Y, Smeets WJ. Integration and segregation of limbic cortico-striatal loops at the thalamic level: an experimental tracing study in rats. Journal of chemical neuroanatomy. 1999;16(3):167–85. pmid:10422737
  81. 81. Elena Erro M, Lanciego JL, Gimenez-Amaya JM. Re-examination of the thalamostriatal projections in the rat with retrograde tracers. Neuroscience research. 2002;42(1):45–55. pmid:11814608
  82. 82. Kessler RM, Woodward ND, Riccardi P, Li R, Ansari MS, Anderson S, et al. Dopamine D2 receptor levels in striatum, thalamus, substantia nigra, limbic regions, and cortex in schizophrenic subjects. Biol Psychiatry. 2009;65(12):1024–31. Epub 2009/03/03. pmid:19251247
  83. 83. Lehrer DS, Christian BT, Mantil J, Murray AC, Buchsbaum BR, Oakes TR, et al. Thalamic and prefrontal FDG uptake in never medicated patients with schizophrenia. Am J Psychiatry. 2005;162(5):931–8. Epub 2005/05/03. pmid:15863795
  84. 84. Goozee R, Handley R, Kempton MJ, Dazzan P. A systematic review and meta-analysis of the effects of antipsychotic medications on regional cerebral blood flow (rCBF) in schizophrenia: association with response to treatment. Neurosci Biobehav Rev. 2014;43:118–36. pmid:24690578
  85. 85. Ishii K. PET approaches for diagnosis of dementia. AJNR American journal of neuroradiology. 2014;35(11):2030–8. pmid:23945233
  86. 86. Aggleton JP, Pralus A, Nelson AJ, Hornberger M. Thalamic pathology and memory loss in early Alzheimer's disease: moving the focus from the medial temporal lobe to Papez circuit. Brain: a journal of neurology. 2016;139(Pt 7):1877–90.