Coordinate-Based Meta-Analysis of the Default Mode and Salience Network for Target Identification in Non-Invasive Brain Stimulation of Alzheimer’s Disease and Behavioral Variant Frontotemporal Dementia Networks

Abstract

Background: The accurate choice of the site of non-invasive brain stimulation (NIBS) is an important factor in trial design.

Objective: Based on the observation that Alzheimer’s disease (AD) and behavioral frontotemporal dementia (bvFTD) affect specific large-scale networks, i.e., the default mode network (DMN) and the salience network (SN), respectively, we aimed to identify population-average coordinates of these networks that could be used as potential targets in NIBS trials aiming to modulate these circuits.

Methods: A systematic literature search of resting-state functional MRI studies reporting DMN and SN stereotactic coordinates was performed according to PRISMA guidelines. Coordinate-based meta-analyses were conducted to identify consistent nodes of the DMN and SN using GingerALE BrainMap software and the activation likelihood estimation method.

Results: DMN coordinates mapped primarily to mesial areas (posterior cingulate cortex/precuneus [Brodmann Area – BA 23/31] and medial prefrontal cortex [BA 9/10/32]). More superficial areas mapped to the bilateral parietal (angular gyrus [BA 39]), temporal (middle gyrus [BA 21]) and dorsolateral prefrontal (superior gyrus [BA 8]) cortex. SN coordinates mapped primarily to mesial and deep frontal areas (anterior insula, anterior cingulate cortex [BA 24/32]), but more superficial areas mapped to the bilateral parietal (supramarginal gyrus [BA 40]) and the right dorsolateral prefrontal (middle gyrus [BA 9/10]) cortex.

Conclusions: NIBS should target the bilateral angular, the middle temporal cortex, or superior frontal gyri in AD for DMN modulation, and the right middle frontal or supramarginal gyri in bvFTD for SN modulation.

Keywords

Alzheimer’s disease behavioral variant frontotemporal dementia default mode network meta-analysis non-invasive brain stimulation salience network

INTRODUCTION

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are two leading causes of dementia for which no therapeutic treatment is available thus far. Numerous pharmacological trials have been carried out in AD targeting the key disease hallmarks, amyloid-β and tau, but results have been disappointing [1]. In FTD, therapeutic options are even more limited due to the clinical and pathological heterogeneity of the disease and to the lack of large-scale randomized clinical trials [2]. The FTD spectrum includes different clinical syndromes as the behavioral variant (bvFTD) and the language variants (primary progressive aphasias) [3, 4].

Non-invasive brain stimulation (NIBS) denotes a class of non-pharmacological interventions increasingly used to treat neurological diseases [5]. The principle behind these approaches is to modulate neuronal activity in specific circuits. The two most common brain stimulation techniques are repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS). rTMS operates by delivering short magnetic impulses to the scalp through a coil; these impulses in turn generate cortical electric currents that can influence neuronal excitability [6]. High-frequency rTMS results mainly in excitatory effects while low-frequency has generally an inhibitory effect [6]. Conversely, tDCS acts by delivering weak electric currents (1-2 mA) via two electrodes located on the scalp, and these currents generate an electric field that can modulate neuronal activity. Differently from rTMS, tDCS does not induce neuronal firing but acts as a neuromodulator. Anodal tDCS increases cortical excitability, whereas cathodal stimulation decreases it [7].

To date, a few studies have investigated the interventional potential of NIBS for the treatment of dementia, reporting a positive effect of high-frequency rTMS or anodal tDCS in AD on cognitive function, including memory [8 –10], language and executive functions [11 –13], and global cognition [14]. A recent meta-analysis reported a statistically significant effect on cognition in AD [15]. In FTD, fewer NIBS studies are available: recent studies in primary progressive aphasia reported an improvement in language after dorsolateral prefrontal cortex (DLPFC) or temporal pole stimulation [16 –20], while in bvFTD no NIBS study has yet been published although pilot studies are underway [21, 22].

Overall, these results seem promising, still the path toward clinically effective stimulation requires further evidence [7]. Two recent reviews cautioned on efficacy of NIBS in dementia, pointing out considerable methodological heterogeneity across studies in stimulation parameters [23, 24]. Among these, the site of stimulation likely has a major influence on treatment efficacy. In depression, a condition for which guidelines for NIBS protocols have been developed and an optimal target for rTMS defined, small displacements from the optimal target are known to produce very variable outcomes [25]. In AD, there is currently no consensus on the optimal target as studies have reported beneficial effects of stimulation adopting variable targets, i.e., the DLPFC [11–13 , 26], the parietal cortex [10], and the temporal cortex [8, 9].

There is increasing consensus that imaging could contribute to the design of targeted, hypothesis-driven, NIBS interventions, including target definition [27 –29]. Resting-state functional MRI (rs-fMRI) studies have identified the networks implicated in AD (default mode network, DMN) and in bvFTD (salience network, SN) [30, 31]. Other networks have been reported to be dysfunctional in AD and bvFTD, such as the executive and frontoparietal (both AD and bvFTD), anterior temporal network (AD), and sensorimotor network (bvFTD) [32 –34]. However, evidence about the involvement of these circuits is less compelling. The DMN is affected early and its topography tracks the clinical severity in AD [30, 35]; similarly, SN topography recapitulates the progression of bvFTD symptoms severity [36, 37]. Moreover, amyloid deposits preferentially in areas of the DMN in AD, while tau-related neurodegeneration affects core areas of the SN in bvFTD [30, 37]. Although no NIBS study to date has targeted the DMN and SN, this background provides a rationale for modulation of these networks in AD and bvFTD [27].

The topography of the DMN is relatively established (core areas include the posterior cingulate cortex (PCC), dorsal and medial prefrontal cortex, inferior parietal lobules, lateral and medial temporal cortex [38]), although some areas are not consistently reported across studies, e.g., the hippocampi and the dorsal prefrontal cortex. Moreover, a precise localization of DMN areas has not yet been established. For example, the lateral temporal cortex includes the inferior, middle, and superior gyri, but it is unclear if one or more of these areas are specifically associated to the DMN. The core regions for SN include the orbital frontoinsular cortices and the anterior cingulate cortex (ACC) [39], while other regions mapping to the frontal and parietal cortices are less consistently reported. Meta-analytic approaches can identify consistent areas across studies and enable a more precise localization of the areas in terms of stereotactic coordinates and Brodmann Areas (BA). In this study, we conducted a coordinate-based quantitative meta-analysis of rs-fMRI studies reporting DMN and SN coordinates to identify consistent, group-average coordinates in normal conditions (healthy subjects) and in patients with dementia (AD and bvFTD compared with controls) that could be used as potential targets for NIBS stimulation. While in principle the best approach to define stimulation targets is the extraction of subject-specific coordinates from individual MRI data, in practice this approach might not be feasible for all centers and all participants. Hence, these results might be useful for researchers aiming to modulate the DMN or SN and do not have the possibility to collect MRI scans.

METHODS

Search strategy

A systematic research of rs-fMRI studies was conducted according to The PRISMA Statement [40]. Our outcomes of interest were the stereotactic coordinates of (i) DMN in healthy subjects (HC) (DMN-HC), (ii) reduced DMN connectivity in AD patients compared to HC (DMN-AD), (iii) SN in HC (SN-HC), and (iv) reduced SN connectivity in bvFTD patients compared to HC (SN-bvFTD). We searched PubMed, EMBASE, and Web of Science databases for rs-fMRI papers published in English between January 2003 (date of the first rs-fMRI study in dementia) and November 2016 using the following combinations of keywords (“default mode network” OR “salience network”; “default mode network” AND “Alzheimer”; “salience network” AND “behavioral frontotemporal”; “resting state” AND “frontotemporal”), and limiting to human studies. Conference proceedings and abstracts were excluded from the search. Further studies were identified through tracing of the retrieved articles and relevant reviews.

Eligibility criteria and study selection

Four classes of publications were defined, according to the above outcomes of interest: (1) DMN-HC; (2) DMN-AD; (3) SN-HC; and (4) SN-bvFTD. To be included, studies had to report rs-fMRI coordinates in Montreal Neurological Institute (MNI) or Talairach space. Exclusion criteria were: (i) non rs-fMRI studies (e.g., task fMRI; mixed task-resting fMRI; PET, MEG, or EEG); (ii) studies reporting only a priori coordinates or a subset of coordinates (e.g., only one hemisphere or an arbitrarily defined number of coordinates); (iii) interventional studies non reporting baseline coordinates; (iv) reviews and meta-analyses; (v) animal studies; (vi) studies investigating networks homologous to the DMN/SN but functionally and anatomically distinct from them, such as the cingulo-opercular network [41]. Moreover, we excluded (vii) studies in adolescents/children (age <18 years) or in mixed adults-adolescents samples, and (viii) studies reporting coordinates in MCI or in pooled patient-control samples.

Data extraction

Study eligibility and data extraction were assessed independently by two reviewers (MP, LP) evaluating full-text articles. Disagreements were solved by consensus. The following variables were extracted: (i) population characteristics (sample size, age, gender, education years), (ii) outcome measure (coordinatesin stereotactic space: [x, y, z]), (iii) additional information related to the outcome (MNI/Talairach space, anatomical label reported in the paper, peaks statistics), (iv) method for network extraction (independent component analysis [ICA] or seed region-of-interest [ROI]), (v) acquisition characteristics (scanner field strength, acquisition time), and (vi) classification (DMN-HC, DMN-AD, SN-HC, SN-bvFTD). Extracted data were managed with Microsoft Excel 2010 v14.

Data redundancy/duplication was avoided as follow. When studies reported both ICA and seed ROI results, ICA-derived coordinates were included as this methodology does not require a priori assumptions. When a study used multiple seed ROIs, we selected the network obtained from (i) the PCC seed for the DMN and (ii) the right insula seed for the SN, as these are the most established seeds for these networks extraction. The PCC seed was always defined as a single mesial ROI except for one study that used both left and right seeds [42]. In this case, both the networks were included. Two studies used the medial prefrontal cortex as seed for the DMN [43, 44]. For studies reporting results both before and after grey matter correction, we reported only the latter. For reliability (test-retest) studies, we reported only coordinates from the first session. For studies assessing independent sub-samples (e.g., younger and older, male and female), we included all the analyses as independent experiments. For studies assessing patients at different disease stages or different genetic groups [45, 46], sub-sample comparisons with controls were treated as independent experiments. Double counting was avoided by checking for possible overlaps in sample, demographics or coordinates reported in studies from the same author or working group. Two studies reporting two overlapping subjects [45, 47] were included in the meta-analysis.

Statistical procedures

Coordinate-based meta-analysis was performed using a quantitative, random-effects meta-analytic method, the activation likelihood estimation (ALE) [48] implemented within GingerALE v2.3.6 (http://www.brainmap.org/ale/). The meta-analysis was conducted in MNI space. Coordinates reported in Talairach space were transformed to MNI using the transformation by Lacadie [49]. If a publication reported Talairach coordinates but did not provide sufficient information about the procedure used (e.g., software, normalization procedure, template), we contacted the corresponding author. If a study reported that the Brett or Lancaster transformation was used to convert MNI coordinates to Talairach space, we used the reverse transformation to obtain the original MNI coordinates.

Briefly, the ALE algorithm models all the foci reported within an experiment as Gaussian probability distributions and creates a ‘modelled activation’ map for the experiment by merging all the foci’s probability distributions. Permutation testing is then used to test the probability that the observed clustering of peak foci from all the experiments was due to chance by comparison with a null distribution of randomly distributed foci. The resulting statistical maps show clusters where convergence between foci is greater than would be expected by chance [48]. Four separate meta-analyses were conducted, according to the publication classifications. Results were thresholded at p < 0.05 corrected for multiple comparisons using cluster-based correction with a cluster-forming threshold of p < 0.001 and 1000 permutations [48]. We excluded clusters in which significant ALE values were driven by less than three studies to ensure robustness of results. The resulting statistical maps were overlaid on the MNI template using Mango v3.0.4 (http://ric.uthscsa.edu/mango/).

Verification on healthy volunteer

An experiment was conducted to verify if group-average coordinates resulting from the meta-analysis could be used at the individual level. To this aim, we arbitrarily chose two cortical areas (one for the DMN and one for the SN) from the meta-analysis and checked on a healthy volunteer whether these targets were anatomically close to the individual rs-fMRI networks. Anatomical landmarks were positioned on an MRI-compatible EEG cap. The EEG cap did not contain electrodes but landmarks corresponding to the EEG 10/10 system. The 10/20 system is conventionally based on the identification of anatomical landmarks like nasion, inion, and the two preauricular points with consecutive placement of the electrodes at fixed distances from these points [50]. This system has been extended to higher density electrode setting (10/10 system) to allow a more precise spatial localization. A healthy volunteer wearing the EEG cap with the two stereotactic markers underwent structural and rs-fMRI imaging on a 3T Philips Achieva system equipped with8-channel head-coil. The subject was instructed to lie still in the scanner, to keep eyes closed but not to fall asleep while images were collected. Two-hundred resting-state volumes were acquired using the following parameters: repetition time (TR) = 3000 ms; echo time (TE) = 30 ms; flip angle = 80°, voxel size = 3 mm isotropic; 48 axial slices; acquisition time = 10 min. Structural 3D T1-weighted image was also acquired (TR = 8 ms; TE = 3.7 ms; flip angle = 8°; voxel size = 1 mm isotropic; 180 sagittal slices; acquisition time = 5 min).

Imaging data were processed by using statistical parametric mapping (SPM8) software (http://www.fil.ion.ucl.ac.uk/spm). Preprocessing consisted of removal of the first five volumes, motion correction and linear spatial normalization to MNI space. The networks of interest (DMN and SN) were extracted with ICA using the Multivariate Exploratory Linear Optimised Decomposition into Independent Components (MELODIC) software (www.fmrib.ox.ac.uk/fsl/melodic; [51]). The components of interest were identified using a goodness-of-fit procedure and the DMN and the SN atlas provided by [52]. The study protocol was approved by the local ethics committee and the participant signed informed consent.

RESULTS

Included studies

A total number of 1,035 publications was initially retrieved (Fig. 1). Based on the inclusion/exclusion criteria, 81 studies were eligible [34 , 53–125]: 57 experiments from 55 studies reporting DMN coordinates in HC (Table 1) with a total of 3,177 subjects (sample range: 6–979; 614 foci); 22 experiments from 19 studies reporting DMN reductions in AD (Table 2) with a total of 493 patients and 500 HC (sample range: 20–120; 138 foci); 23 experiments from 22 studies reporting SN coordinates in HC (Table 3) with 835 subjects (sample range: 12–173; 200 foci); and 5 experiments from four studies reporting SN reductions in bvFTD (Table 4) with 60 patients and 72 HC (sample range: 24–42; 36 foci). Eighteen studies were eligible for two or more meta-analyses as they reported coordinates for both networks and/or both HC and patients. All the meta-analyses except that for SN-bvFTD included the minimum number of experiments (n = 20) required to achieve power with GingerALE [126]. Given the lack of power for the SN-bvFTD meta-analysis, we carried out an exploratory analysis on the available data and reported these results in the Supplementary Material.

Fig.1

PRISMA flow chart of the literature search.

Table 1

Resting-fMRI studies included in the meta-analysis of the default mode network (DMN) in healthy adults

Reference	Experiment details
	Sample	Gender	Age	Reference	Method of	Foci (n)
	size	(% Male)	(years±SD)	space	analysis
Archer [53]	173	57%	21–79^*	MNI	ICA	03
Balaev [54]	15	53%	68±5	MNI	ICA	8
Beucke [55]	46	43%	30±9	MNI	Seed ROI	9
Bluhm [56]	40	43%	29±11	MNI	ICA	6
Bluhm [57]	15	27%	24±5	MNI	Seed ROI	9
Bluhm [58]	15	0%	38±13	MNI	Seed ROI	15
Boly [59]	06	NA	41±11	MNI	Seed ROI	8
Bonavita [60]	18	44%	39±10	TAL	ICA	4
Bonhage [43]	26	58%	27±3	MNI	Seed ROI	19
Bryant [61]	37	35%	21±2	MNI	ICA	17
C $\overset{´}{a}$ ceda [62]	29	38%	25±3	MNI	ICA	3
Cha [63]	62	27%	69±8	MNI	ICA	13
De Havas [64]	26	42%	23±2	TAL	Seed ROI	7
De Vogelaere [65]	16	62%	62±7	MNI	ICA	13
Doll [66]	16	6%	34±NA	MNI	ICA	9
Franciotti [67]	15	33%	74±2	TAL	ICA	9
Fransson [68]	13	54%	20–38^*	MNI	Seed ROI	21
Glahn [69]	333	37%	48±13	MNI	ICA	8
Gordon [70]	44	43%	19±1	MNI	ICA	6
Gour [34]	14	36%	59±6	MNI	Seed ROI	30
Grady [71]	36	NA	48±4	MNI	Seed ROI	7
Grady [72]	28	46%	70±NA	MNI	Seed ROI	8
Greicius [73]	14	50%	21±NA	TAL	ICA	8
Harrison [74]	22	45%	26±4	TAL	ICA	8
Huijbers [75]	22	36%	23±NA	MNI	ICA	7
James [76]	23	39%	39±13	MNI	ICA	10
Koch [77]	38	45%	48±5	TAL	ICA	10
Korgaonkar [78]	27	56%	25±6	MNI	Seed ROI	5
Kucyi [44]	17	0%	32±10	MNI	Seed ROI	10
Liao [79]	19	74%	22±4	MNI	ICA	21
Lim [80]	56	34%	76±6	MNI	ICA	9
Lois [81]	35	43%	42±8	MNI	ICA	8
Ma [82]	14	100%	38±8	MNI	ICA	8
Manoliu [83]	25	44%	44±15	MNI	ICA	11
Manoliu [84]	20	45%	34±13	MNI	ICA	8
Mantini [85]	15	100%	20–29^*	TAL	ICA	10
Meindl [86]	18	56%	28±NA	TAL	ICA	6
Mevel [87]	70	30%	44±18	MNI	ICA	13
Mohammadi [88]	20	50%	57±NA	TAL	ICA	8
Myers [89]	12	44%	64±5	MNI	ICA	14
Otti [90]	19	37%	49±12	MNI	ICA	2
Passow [91]	08	100%	53±11	MNI	ICA	4
Quarantelli [92]	22	50%	39±14	MNI	Seed ROI	26
Shehzad [93]	26	45%	21±8	MNI	Seed ROI	12
Sours [94]	14	57%	37±14	MNI	Seed ROI	7
Sripada [95]	15	100%	21–37^*	MNI	Seed ROI	18
Tomasi [96]	979	NA	18–71^*	TAL	Seed ROI	22
Uddin [97]	26	65%	28±9	TAL	Seed ROI	16
van Buuren [98]	18	39%	28±8	MNI	Seed ROI	6
Vanhaudenhuyse [99]	14	NA	28–57^*	MNI	ICA	16
Vidal-Piñeiro [100]	116	32%	68±3	MNI	ICA	6
50	50%	46±3	MNI	ICA	6
Voss [101]	59	53%	22±NA	MNI	ICA	13
Wang [102]	243	47%	22±1	MNI	Seed ROI	11
Weissman-Fogel [103]	26	0%	30±8	TAL	ICA	15
23	100%	30±9	TAL	ICA	20
Zhang [104]	29	55%	25±7	MNI	ICA	8

^*Values denote range. ICA, independent component analysis; MNI, Montreal Neurological Institute; NA, not available; ROI, region of interest; TAL, Talairach.

Table 2

Resting-fMRI studies included in the meta-analysis of the default mode network (DMN) in Alzheimer’s Disease (AD) compared with healthy controls (HC)

Reference	Experiment details
	AD				HC
	Sample	Gender	Age	MMSE	Sample	Reference	Method of	Foci (n)
	size	(% Male)	(years±SD)	(±SD)	size	space	analysis
Balthazar [105]	20	NA	74±8	16±6	17	MNI	ICA	2
Binnewijzend [106]	39	59%	67±8	22±3	43	MNI	ICA	3
Cha [63]	37	27%	73±8	17±7	62	MNI	ICA	4
Chhatwal [107]	83	37%	43±10	026±NA	37	MNI	ICA	22
Damoiseaux [108]	21	43%	64±9	23±4	18	MNI	ICA	1
Filippi [109]	18	56%	61±6	20±4	30	MNI	ICA	1
Gili [110]	11	64%	72±8	20±5	10	MNI	Seed ROI	6
Gour [34]	14	43%	75±3	21±3	14	MNI	Seed ROI	7
	14	64%	60±6	19±4	14	MNI	Seed ROI	9
Hafkemeijer [111]	31	61%	65±7	23±3	29	MNI	ICA	3
Jones [112]	28	64%	078±NA	NA	56	MNI	ICA	2
Liang [113]	9	33%	65±10	16±4	11	MNI	ICA	8
Myers [89]	23	61%	69±7	NA	12	MNI	ICA	1
Song [114]	35	49%	66±8	10±7	21	MNI	ICA	2
Weiler [115]	22	27%	73±6	19±5	26	MNI	Seed ROI	4
Yi [116]	11	9%	064±NA	18±1	12	MNI	ICA	2
Zhang [46]	16	38%	72±5	22±4	16	MNI	Seed ROI	9
	11	55%	77±8	13±4	16	MNI	Seed ROI	9
	12	50%	72±7	3±4	16	MNI	Seed ROI	10
Zhang [117]	16	38%	72±5	22±4	16	MNI	Seed ROI	11
Zhou [47]	12	42%	63±8	21±5	12	MNI	ICA	13
Zhu [42]	10	70%	73±8	20±4	12	MNI	Seed ROI	9

ICA, independent component analysis; MMSE, Mini-Mental State Examination; MNI, Montreal Neurological Institute; NA, not available; ROI, region of interest; TAL, Talairach.

Table 3

Resting-fMRI studies included in the meta-analysis of the salience network (SN) in healthy adults

Reference	Experiment details
	Sample	Gender	Age	Reference	Method of	Foci (n)
	size	(% Male)	(years±SD)	space	analysis
Archer [53]	173	57%	21–79^*	MNI	ICA	1
Bonhage [43]	26	58%	27±3	MNI	Seed ROI	16
Bryant [61]	37	35%	21±2	MNI	ICA	9
Cáceda [62]	29	38%	25±3	MNI	ICA	3
Caulfield [118]	26	27%	21±2	MNI	Seed ROI	4
Doll [66]	16	6%	034±NA	MNI	ICA	14
Geiger [119]	63	48%	29±8	MNI	Seed ROI	10
Grady [71]	28	46%	71±3	MNI	Seed ROI	7
Kucyi [120]	28	54%	29±4	MNI	ICA	8
Lim [80]	56	34%	76±6	MNI	ICA	3
Lois [81]	35	43%	42±8	MNI	ICA	9
Manoliu [83]	25	44%	44±15	MNI	ICA	4
Markett [121]	23	0%	22±5	MNI	Seed ROI	6
Myers [89]	12	44%	64±5	MNI	ICA	6
Otti [90]	19	37%	49±12	MNI	ICA	6
Passamonti [122]	45	50%	29±6	MNI	ICA	11
Qiu [123]	21	95%	18–52^*	MNI	ICA	6
Seeley [39]	21	48%	35±16	MNI	ICA	27
Sorg [124]	21	48%	34±14	MNI	ICA	11
Sripada [95]	15	100%	21–37^*	MNI	Seed ROI	7
Voss [101]	59	53%	022±NA	MNI	ICA	8
Weissman-Fogel [103]	26	0%	30±8	TAL	ICA	12
	23	100%	30±9	TAL	ICA	12

^*Values denote range. ICA, independent component analysis; MNI, Montreal Neurological Institute; ROI, region of interest; TAL, Talairach.

Table 4

Resting-fMRI studies included in the meta-analysis of the salience network (SN) in behavioral variant frontotemporal dementia (bvFTD) compared with healthy controls (HC)

Reference	Experiment details
	bvFTD				HC
	Sample	Gender	Age	MMSE	Sample	Reference	Method of	Foci (n)
	size	(% Male)	(years±SD)	±SD)	size	space	analysis
Farb [125]	8	50%	67±3	NA	16	MNI	ICA	6
Filippi [109]	12	66%	59±8	23±6	30	MNI	ICA	2
Lee [45]	14	71%	58±8	25±4	14	MNI	Seed ROI	7
	14	71%	61±7	26±4	14	MNI	Seed ROI	5
Zhou [47]	12	50%	61±5	26±4	12	MNI	ICA	16

ICA, independent component analysis; MMSE, Mini-Mental State Examination; MNI, Montreal Neurological Institute; NA, not available; ROI, region of interest.

Default mode network

The meta-analysis of DMN connectivity patterns in HC revealed 11 consistent clusters (Table 5, Fig. 2). The largest clusters mapped to the PCC/precuneus (BA 23/31), the medial prefrontal cortex, including the orbitofrontal cortex and ACC (BA 9/10/32) and the bilateral angular gyrus (BA 39) (Fig. 2). GingerALE report indicated that 70% of the included studies contributed to these clusters. Further clusters mapped to the middle temporal gyrus (BA 21), the medial temporal cortex bilaterally, including the hippocampal formation (BA 28/35/36), and the superior frontal gyrus (BA 8) bilaterally (Fig. 2). Between 43% and 21% of studies contributed to these areas. A smaller cluster mapped to the left cerebellum; 8 studies (15%) contributed to this cluster.

Table 5

DMN clusters in healthy adults and AD patients

	Brain Region	BA	MNI coordinates			Cluster size	Peak ALE
			x	y	z	(mm³)	(×10^–2)
HC
	Medial Prefrontal Cortex	9/10/32	0	54	–6	12184	7.93
			0	56	16	4.92
	PCC/Precuneus	23/31	0	–56	26	11768	11.34
	Angular Gyrus L	39	–46	–64	30	6648	8.59
	Angular Gyrus R	39	50	–62	28	6248	11.66
	Middle Temporal Gyrus R	21	62	–8	–20	3944	8.80
	Middle Temporal Gyrus L	21	–60	–12	–20	3760	8.32
	Medial Temporal Cortex L	28/35/36	–26	–36	–14	3248	4.77
			–22	–16	–22		3.79
			–24	–24	–20		2.92
	Medial Temporal Cortex R	28/35/36	24	–18	–18	2224	5.79
			28	–32	–14		2.18
	DLPFC (Superior Gyrus) R	8	22	26	50	2552	3.91
	DLPFC (Superior Gyrus) L	8	–24	24	54	2452	3.57
			–22	32	46		3.05
Cerebellum	NA	–30	–80	–36	0960	4.56
AD <HC
	PCC/Precuneus	23/31	04	–64	28	4464	2.47
			–2	–64	28		2.40
			–10	–46	30		1.88
			4	–50	28		1.79
			–2	–58	40		1.70
			–2	–44	42		1.64
			0	–56	20		1.41

AD, Alzheimer’s disease; BA, Brodmann area; DLPFC, dorsolateral prefrontal cortex; HC, healthy controls; L, left; MNI, Montreal Neurological Institute; PCC, posterior cingulate cortex; R, right. Results of the Activation Likelihood Estimation (ALE) meta-analysis of default mode network (DMN) coordinates in HC and in AD patients compared with HC (AD <HC).

Fig.2

Nodes of the default mode network (DMN) and salience network (SN) identified through the meta-analysis of rs-fMRI studies in healthy adults. In the left panel, nodes of DMN (red) and SN (green) are overlaid on the standard MNI template. In the right panel, nodes of DMN and SN are overlaid on the 3D MNI template to display their superficial cortical spatial distribution. Color bars denote ALE values that represent convergence of foci from different experiments. Higher values denote higher convergence. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

In AD, the PCC/precuneus (BA 23/31) consistently showed reduced connectivity (Table 5, Fig. 3). More than 70% of the studies included contributed to the PCC/precuneus cluster. No area of reduced connectivity was detected in other DMN areas, such as the medial and dorsal prefrontal cortex, the angular and middle temporal gyri, or the hippocampal formation.

Fig.3

Nodes of reduced default mode network connectivity in Alzheimer’s disease patients relative to controls identified through the meta-analysis. Clusters are overlaid on the standard MNI template. Color bars denote ALE values that represent convergence of foci from different experiments. Higher values denote higher convergence. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

Salience network

Consistent SN clusters in HC mapped to the bilateral insula, the medial ACC (BA 24/32), the bilateral supramarginal gyrus (BA 40), and the right anterior prefrontal cortex (middle frontal gyrus, BA 9/10) (Table 6, Fig. 2). GingerALE report indicated that all studies but one contributed to the right anterior insula cluster. The majority of studies contributed to the left insula and ACC (81% and 63%, respectively), 59% contributed to the right supramarginal gyrus, and 41% contributed to the right prefrontal cluster. Only four studies (18%) contributed to the left supramarginal gyrus.

Table 6

SN clusters in healthy adults

Brain Region	BA	MNI Coordinates			Cluster size	Peak ALE
		x	y	z	(mm³)	(×10^–2)
Anterior Insula R	NA	40	14	–2	5400	5.14
ACC	24/32	06	30	28	4888	3.69
		4	24	42		1.99
Anterior Insula L	NA	–40	16	–2	5176	5.14
		–34	14	8		2.24
		–24	12	–6		1.91
		–48	18	–10		1.83
Supramarginal Gyrus R	40	58	–40	30	2992	3.56
DLPFC (Middle Gyrus) R	9/10	32	50	22	2000	3.43
		42	36	26		1.66
Supramarginal Gyrus L	40	–60	–40	36	744	2.41

ACC, anterior cingulate cortex; BA, Brodmann area; DLPFC, dorsolateral prefrontal cortex; L, left; MNI, Montreal Neurological Institute; NA, not applicable; R, right. Results of the Activation Likelihood Estimation (ALE) meta-analysis of salience network (SN) coordinates in healthy adults.

Verification on healthy volunteer

Based on the clusters identified by the meta-analysis, we defined the following superficial cortical areas: the right angular gyrus for the DMN and the right middle frontal gyrus for the SN. The right angular marker was located above the P4-P6 position, while the right anterior prefrontal mark over the Fp2-AF4 position. Visual inspection of subject MRI functional data showed a good anatomical correspondence between the meta-analysis-derived locations and the single subject rs-fMRI networks (Fig. 4).

Fig.4

Experiment to verify whether group-average coordinates derived from the meta-analysis can be used at the individual level. Two cortical areas (one for the DMN – right angular gyrus – and one for the SN – right middle frontal gyrus) were arbitrarily chosen from the meta-analysis. Anatomical landmarks corresponding to these areas were positioned on an MRI-compatible EEG cap (Fp2-AF4 for the SN node, P4-P6 for the DMN node). The EEG cap did not contain electrodes but landmarks corresponding to the EEG 10/10 system. Maps show the SN (blue) and DMN (red) as extracted from a healthy volunteer (HC) undergoing rs-fMRI scan while wearing the EEG cap with two stereotactic markers (fish-oil capsules, filled circles) located in the above areas. The white dots show that the stereotactic markers are adjacent to the target network. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

DISCUSSION

DMN and SN topography

To our knowledge, this is the first meta-analysis focused on rs-fMRI studies of the DMN. The DMN areas identified by our meta-analysis in HC are in line with those reported by previous reviews and meta-analysis of task fMRI studies [38, 127]. However, compared with previous meta-analysis [127], we focused on resting-state studies and have therefore avoided task-induced alterations in DMN topography [128] and possibly detected more robust coordinates as DMN connectivity is stronger during rest than during task [129]. The PCC and medial prefrontal cortex are invariably reported as core areas of the DMN and our analysis confirmed this. Dorsal parietal and temporal areas are also consistently reported as part of the DMN [38] and the meta-analysis enabled us to precisely map these DMN sites to the middle temporal gyrus (BA 21) and angular gyrus (BA 49). Also, our analysis confirmed that the medial temporal cortex (BA 28/35/36) and dorsal frontal cortex (BA 8), two areas inconsistently reported across fMRI studies, are part of the DMN.

Regarding SN, this is the first meta-analysis reporting SN coordinates in HC. Our analysis recognized the ACC and bilateral anterior insulae as core areas of this network, in line with the first description of this circuit by Seeley et al. [39]. The anterior insula and ACC are known to be strongly interconnected and to modulate behavior through the integration of limbic and autonomic information in relation with social rules [39]. Differently from earlier studies, the meta-analysis did not identify the basal ganglia or striatal/limbic areas as part of the SN. Instead, two cortical areas were consistently identified, the supramarginal gyri and the middle frontal gyrus. These areas have been described as part of a control network that is involved in task maintenance and sustained attention on task-fMRI [130]; however, our analysis suggests that these regions are part of the SN even when no stimulus is present. The SN showed a preferential right lateralization, as clusters in the left hemisphere were overall smaller that their homologous counterpart (insula and supramarginal gyrus) or missing (middle frontal gyrus). Overall, this asymmetry is in line with the dominant role of the right hemisphere in emotion control and has important implication for the definition ofSN targets.

Targets for DMN stimulation

The meta-analysis of DMN in HC identified a number of medial/deep areas that are not reachable by NIBS, such as the posterior and anterior cingulate and the medial temporal lobes. Superficial areas were the bilateral angular gyrus and bilateral middle temporal gyrus. These areas are reachable with NIBS and their MNI coordinates might aid to design DMN stimulation paradigms with rTMS in AD. For tDCS, which uses large targets, these cortical areas correspond on the EEG 10/10 system to areas P3-P5 and P4-P6 for the left and right angular gyrus, and areas T7-TP7 and T8-TP8 for the left and right middle temporal gyrus. Considering previous tDCS studies in AD, some investigators defined targets closely corresponding to these sites and possibly stimulated the DMN. Boggio et al. [8] stimulated area T7 and showed positive effect on visual recognition memory task and a subsequent study by the same group [9] showed a similar effect after stimulation of the bilateral temporal cortex (T7 and T8). Ferrucci et al. [10] defined the temporo-parietal cortex (positions P3-T5 and P6-T4, which include the angular gyrus and extend to the temporal cortex) as targets of tDCS stimulation and showed beneficial effects on verbal recognition memory. Other studies generally stimulated the DLPFC and it is difficult to establish whether this target corresponds to the frontal DMN region identified by our meta-analysis, i.e., the superior frontal gyrus, or to other networks, such as the fronto-parietal and salience networks [131]. The frontal area we extracted roughly corresponds to areas F1-F3 and F2-F4 and was overall two- or three-fold smaller than parietal and temporal clusters. If this region is defined as target for DMN stimulation, careful positioning of the electrodes/magnet is necessary to reach this relatively small node and to avoid off-targetstimulation.

A possible issue with the above cortical targets is that these areas were not identified as affected in AD. Indeed, the meta-analysis in patients did not detect reduced connectivity in frontal and angular DMN clusters. If efficacy of NIBS was greatest when targeting the most affected areas, then DMN modulation in AD would be problematic. Conversely, if efficacy was greatest when targeting areas with residual function, then the proposed areas might be suitable targets. We are currently testing the efficacy of the above approach and future studies will enable to properly address this issue.

Targets for SN stimulation

The main SN clusters in HC were located in areas not reachable with NIBS (ACC and insulae); however, superficial areas were also detected: the right middle frontal gyrus and the bilateral supramarginal gyri. These areas roughly correspond to Fp2-AF4 (right middle frontal gyrus) and CP4-CP6/CP3-CP5 (bilateral supramarginal gyri) positions on the EEG 10/10 system and might represent possible targets of SN stimulation. The size of these clusters was two- or three-fold smaller than that of core SN areas, and that of the left supramarginal gyrus was especially small, with only three studies contributing to this cluster. Thus, the right frontal and parietal clusters seem better choices for stimulation of the SN. Precise localization of these targets is necessary considering their relatively small size and their proximity to anti-correlated networks (e.g., DMN’s frontal and parietal clusters). Strategies to increase stimulation focality are necessary, e.g., the use of neuronavigation systems or ad-hoc strategies to reduce the area of the sponge meanwhile reducing the intensity of thecurrent, or to increase the size of the reference electrode [132].

Additional considerations

One key aspect in the design of NIBS interventions is the paradigm, i.e., whether excitatory or inhibitory stimulation is used [29]. In AD, there is general agreement that excitatory (high-frequency rTMS or anodal tDCS) stimulation has a positive effect on cognition, while inhibition (low-frequency rTMS or cathodal tDCS) worsens performance [10 , 26]. The DMN includes areas that are associated with memory (medial temporal lobe) and associative functions (temporo-parietal and frontal dorsal cortex), thus excitatory stimulation of this network seems a plausible paradigm. However, to date no data is available on the effect of DMN stimulation in AD. A study in healthy adults showed that a single session of high-frequency rTMS stimulation of the parietal DMN reduced connectivity with cortical DMN nodes, while low-frequency rTMS increased connectivity with the hippocampus but not with cortical nodes [133]. Whether this paradigm would affect positively cognition in AD is currently unknown and studies are needed to define the appropriate paradigm of DMN stimulation in AD.

While excitatory stimulation of affected networks is the most common approach in dementia, other paradigms are in principle possible, e.g., inhibition of hyper-connected networks. Zhou et al. [47] showed that the SN is hyper-connected in AD while the DMN is hyper-connected in bvFTD, thus suppression of the hyper-connected network in each condition might be an alternative approach. This has indeed been observed in obsessive-compulsive disorder, where the orbitofrontal cortex shows increased connectivity [134] and suppression of hyper-connectivity with low-frequency rTMS was an effective intervention [135]. In line with this view, a recent rTMS study reported improved memory in MCI after inhibition of the right DLPFC (BA 9) [136], an area roughly corresponding to the right middle frontal gyrus we identified as part of the SN. A third, alternative, approach might be the combined inhibition of hyper-connected networks with stimulation of hypo-connected networks. This approach has been recently tested in post-stroke patients with aphasia, where the hypo-functional hemisphere was stimulated using anodal tDCS and the contralateral hyperactive areas were inhibited using cathodal stimulation [137, 138]. Nevertheless, caution is needed when designing inhibitory paradigms as it is not clear if hyper-connectivity reflects compensatory or detrimental mechanisms. Moreover, the effect of stimulation at the molecular level is currently unknown. Preliminary evidence from animal models suggests that excitatory/inhibitory stimulation might modulate the deposition of toxic proteins differentially. Cirrito et al. [139] reported that stimulation of the perforant pathway in a mice model of AD leads to increased amyloid-β deposition, while inhibition has the opposite effect. The effect of stimulation on tau pathology is unknown. Considering the lack of studies testing i) the effect of inhibitory or combined excitatory/inhibitory stimulation in dementia and ii) the effect of NIBS on molecular pathology, further investigations seem necessary to identify the most appropriate NIBS paradigm.

One limitation of NIBS is that it cannot reach deep cortical areas [140], thus targets should be selected as superficial as possible. In our meta-analysis, the largest hubs were not superficial (e.g., PCC and medial prefrontal for the DMN, insulae and ACC for the SN), a result that might be viewed as a limitation to the implementation of NIBS in these conditions. However, in a network-based perspective the key factor for efficacy of NIBS seems to be the presence of connectivity between network areas rather than the possibility to reach the largest cluster. Hence, when the primary nodes are not reachable, other cortical nodes might serve the purpose. This view is supported by recent work by Fox et al. [141] that showed noninvasive stimulation is effective when targeting areas that are functionally connected to targets of effective stimulation within the same network [141].

Finally, there are other factors that need to be considered when designing NIBS for dementia and that might affect response to treatment, such as APOE and age. APOE is the greatest genetic risk factor for AD and could differentially modulate DMN response to stimulation [142], while age at onset is an important determinant of the clinical phenotype and response to stimulation of the memory circuits seems to vary according to age [143].

Strengths and limitations

Some strengths of the study should be mentioned. First, this study is the first attempt to link imaging data to the design of targeted NIBS interventions for neurodegenerative diseases. Also, this study is the first one to report SN coordinates. Among limitations, the major weakness is that few studies were available for the meta-analysis in bvFTD. This, together with the paucity of studies in bvFTD, suggests caution in the implementation of NIBS interventions targeting the SN in this population. In addition, the identified coordinates might not be generalizable to all patients because of individual differences in anatomy/function, although these results might be useful to clinical centers without MRI facilities and/or for those subjects who are unable to undergo MRI evaluations. Third, a limitation of NIBS is that it cannot reach deep areas. Stronger current/field can potentially reach deeper areas, but this would be unsafe. More sophisticated techniques such as deep TMS could reach deeper areas while keeping electric fields within safe limits [144], but at the price of less focal stimulation. Fourth, future meta-analyses could examine the topography of other networks that have been reported as affected in dementia, such as the frontoparietal, language, and executive networks. Finally, in this meta-analysis we considered AD and bvFTD as homogenous clinical groups, however AD can present with atypical variants (e.g., logopenic, posterior cortical atrophy) and bvFTD also shows clinical variability (e.g., apathy, agitation). The topography of network dysfunction is likely to differ accordingly [145]. Similarly, factors such as age at onset, APOE, and clinical stage can affect network dysfunction [34 , 147] and it would be interesting to know how these factors modulate network topography. However, at present the available studies do not provide sufficient power to conduct separate meta-analysis with GinglerALE. This issue will likely be addressed by future meta-analysis, when more studies will be published.

Conclusion

This meta-analysis identified several potential targets of NIBS for DMN and SN stimulation. These results could represent a starting point for the design of targeted and effective NIBS interventions in dementia and possibly help to reduce heterogeneity between studies.

Footnotes

ACKNOWLEDGMENTS

This work was supported by the Italian Ministry of Health (Giovani Ricercatori grant GR2011-02349787 and Ricerca Corrente).

Authors’ disclosures available online ().

Appendix

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